Amazon SageMaker Service

2023/12/15 - Amazon SageMaker Service - 1 new 49 updated api methods

Changes  This release 1) introduces a new API: DeleteCompilationJob , and 2) adds InfraCheckConfig for Create/Describe training job API

DeleteCompilationJob (new) Link ¶

Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.

You can delete a compilation job only if its current status is COMPLETED , FAILED , or STOPPED . If the job status is STARTING or INPROGRESS , stop the job, and then delete it after its status becomes STOPPED .

See also: AWS API Documentation

Request Syntax

client.delete_compilation_job(
    CompilationJobName='string'
)
type CompilationJobName

string

param CompilationJobName

[REQUIRED]

The name of the compilation job to delete.

returns

None

AddAssociation (updated) Link ¶
Changes (request)
{'AssociationType': {'SameAs'}}

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking .

See also: AWS API Documentation

Request Syntax

client.add_association(
    SourceArn='string',
    DestinationArn='string',
    AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs'
)
type SourceArn

string

param SourceArn

[REQUIRED]

The ARN of the source.

type DestinationArn

string

param DestinationArn

[REQUIRED]

The Amazon Resource Name (ARN) of the destination.

type AssociationType

string

param AssociationType

The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.

  • ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.

  • AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.

  • DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.

  • Produced - The source generated the destination. For example, a training job produced a model artifact.

rtype

dict

returns

Response Syntax

{
    'SourceArn': 'string',
    'DestinationArn': 'string'
}

Response Structure

  • (dict) --

    • SourceArn (string) --

      The ARN of the source.

    • DestinationArn (string) --

      The Amazon Resource Name (ARN) of the destination.

BatchDescribeModelPackage (updated) Link ¶
Changes (response)
{'ModelPackageSummaries': {'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
                                                                                                  'ml.c7i.16xlarge',
                                                                                                  'ml.c7i.24xlarge',
                                                                                                  'ml.c7i.2xlarge',
                                                                                                  'ml.c7i.48xlarge',
                                                                                                  'ml.c7i.4xlarge',
                                                                                                  'ml.c7i.8xlarge',
                                                                                                  'ml.c7i.large',
                                                                                                  'ml.c7i.xlarge',
                                                                                                  'ml.dl1.24xlarge',
                                                                                                  'ml.m7i.12xlarge',
                                                                                                  'ml.m7i.16xlarge',
                                                                                                  'ml.m7i.24xlarge',
                                                                                                  'ml.m7i.2xlarge',
                                                                                                  'ml.m7i.48xlarge',
                                                                                                  'ml.m7i.4xlarge',
                                                                                                  'ml.m7i.8xlarge',
                                                                                                  'ml.m7i.large',
                                                                                                  'ml.m7i.xlarge',
                                                                                                  'ml.r7i.12xlarge',
                                                                                                  'ml.r7i.16xlarge',
                                                                                                  'ml.r7i.24xlarge',
                                                                                                  'ml.r7i.2xlarge',
                                                                                                  'ml.r7i.48xlarge',
                                                                                                  'ml.r7i.4xlarge',
                                                                                                  'ml.r7i.8xlarge',
                                                                                                  'ml.r7i.large',
                                                                                                  'ml.r7i.xlarge',
                                                                                                  'ml.trn1n.32xlarge'}}}}

This action batch describes a list of versioned model packages

See also: AWS API Documentation

Request Syntax

client.batch_describe_model_package(
    ModelPackageArnList=[
        'string',
    ]
)
type ModelPackageArnList

list

param ModelPackageArnList

[REQUIRED]

The list of Amazon Resource Name (ARN) of the model package groups.

  • (string) --

rtype

dict

returns

Response Syntax

{
    'ModelPackageSummaries': {
        'string': {
            'ModelPackageGroupName': 'string',
            'ModelPackageVersion': 123,
            'ModelPackageArn': 'string',
            'ModelPackageDescription': 'string',
            'CreationTime': datetime(2015, 1, 1),
            'InferenceSpecification': {
                'Containers': [
                    {
                        'ContainerHostname': 'string',
                        'Image': 'string',
                        'ImageDigest': 'string',
                        'ModelDataUrl': 'string',
                        'ProductId': 'string',
                        'Environment': {
                            'string': 'string'
                        },
                        'ModelInput': {
                            'DataInputConfig': 'string'
                        },
                        'Framework': 'string',
                        'FrameworkVersion': 'string',
                        'NearestModelName': 'string',
                        'AdditionalS3DataSource': {
                            'S3DataType': 'S3Object',
                            'S3Uri': 'string',
                            'CompressionType': 'None'|'Gzip'
                        }
                    },
                ],
                'SupportedTransformInstanceTypes': [
                    'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                ],
                'SupportedRealtimeInferenceInstanceTypes': [
                    'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
                ],
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedResponseMIMETypes': [
                    'string',
                ]
            },
            'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
            'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
        }
    },
    'BatchDescribeModelPackageErrorMap': {
        'string': {
            'ErrorCode': 'string',
            'ErrorResponse': 'string'
        }
    }
}

Response Structure

  • (dict) --

    • ModelPackageSummaries (dict) --

      The summaries for the model package versions

      • (string) --

        • (dict) --

          Provides summary information about the model package.

          • ModelPackageGroupName (string) --

            The group name for the model package

          • ModelPackageVersion (integer) --

            The version number of a versioned model.

          • ModelPackageArn (string) --

            The Amazon Resource Name (ARN) of the model package.

          • ModelPackageDescription (string) --

            The description of the model package.

          • CreationTime (datetime) --

            The creation time of the mortgage package summary.

          • InferenceSpecification (dict) --

            Defines how to perform inference generation after a training job is run.

            • Containers (list) --

              The Amazon ECR registry path of the Docker image that contains the inference code.

              • (dict) --

                Describes the Docker container for the model package.

                • ContainerHostname (string) --

                  The DNS host name for the Docker container.

                • Image (string) --

                  The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

                  If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

                • ImageDigest (string) --

                  An MD5 hash of the training algorithm that identifies the Docker image used for training.

                • ModelDataUrl (string) --

                  The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

                  Note

                  The model artifacts must be in an S3 bucket that is in the same region as the model package.

                • ProductId (string) --

                  The Amazon Web Services Marketplace product ID of the model package.

                • Environment (dict) --

                  The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

                  • (string) --

                    • (string) --

                • ModelInput (dict) --

                  A structure with Model Input details.

                  • DataInputConfig (string) --

                    The input configuration object for the model.

                • Framework (string) --

                  The machine learning framework of the model package container image.

                • FrameworkVersion (string) --

                  The framework version of the Model Package Container Image.

                • NearestModelName (string) --

                  The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

                • AdditionalS3DataSource (dict) --

                  The additional data source that is used during inference in the Docker container for your model package.

                  • S3DataType (string) --

                    The data type of the additional data source that you specify for use in inference or training.

                  • S3Uri (string) --

                    The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

                  • CompressionType (string) --

                    The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

            • SupportedTransformInstanceTypes (list) --

              A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

              This parameter is required for unversioned models, and optional for versioned models.

              • (string) --

            • SupportedRealtimeInferenceInstanceTypes (list) --

              A list of the instance types that are used to generate inferences in real-time.

              This parameter is required for unversioned models, and optional for versioned models.

              • (string) --

            • SupportedContentTypes (list) --

              The supported MIME types for the input data.

              • (string) --

            • SupportedResponseMIMETypes (list) --

              The supported MIME types for the output data.

              • (string) --

          • ModelPackageStatus (string) --

            The status of the mortgage package.

          • ModelApprovalStatus (string) --

            The approval status of the model.

    • BatchDescribeModelPackageErrorMap (dict) --

      A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

      • (string) --

        • (dict) --

          The error code and error description associated with the resource.

          • ErrorCode (string) --

          • ErrorResponse (string) --

CreateAlgorithm (updated) Link ¶
Changes (request)
{'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
                                                                        'ml.c7i.16xlarge',
                                                                        'ml.c7i.24xlarge',
                                                                        'ml.c7i.2xlarge',
                                                                        'ml.c7i.48xlarge',
                                                                        'ml.c7i.4xlarge',
                                                                        'ml.c7i.8xlarge',
                                                                        'ml.c7i.large',
                                                                        'ml.c7i.xlarge',
                                                                        'ml.dl1.24xlarge',
                                                                        'ml.m7i.12xlarge',
                                                                        'ml.m7i.16xlarge',
                                                                        'ml.m7i.24xlarge',
                                                                        'ml.m7i.2xlarge',
                                                                        'ml.m7i.48xlarge',
                                                                        'ml.m7i.4xlarge',
                                                                        'ml.m7i.8xlarge',
                                                                        'ml.m7i.large',
                                                                        'ml.m7i.xlarge',
                                                                        'ml.r7i.12xlarge',
                                                                        'ml.r7i.16xlarge',
                                                                        'ml.r7i.24xlarge',
                                                                        'ml.r7i.2xlarge',
                                                                        'ml.r7i.48xlarge',
                                                                        'ml.r7i.4xlarge',
                                                                        'ml.r7i.8xlarge',
                                                                        'ml.r7i.large',
                                                                        'ml.r7i.xlarge',
                                                                        'ml.trn1n.32xlarge'}},
 'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.c6i.12xlarge',
                                                              'ml.c6i.16xlarge',
                                                              'ml.c6i.24xlarge',
                                                              'ml.c6i.2xlarge',
                                                              'ml.c6i.32xlarge',
                                                              'ml.c6i.4xlarge',
                                                              'ml.c6i.8xlarge',
                                                              'ml.c6i.xlarge',
                                                              'ml.m6i.12xlarge',
                                                              'ml.m6i.16xlarge',
                                                              'ml.m6i.24xlarge',
                                                              'ml.m6i.2xlarge',
                                                              'ml.m6i.32xlarge',
                                                              'ml.m6i.4xlarge',
                                                              'ml.m6i.8xlarge',
                                                              'ml.m6i.large',
                                                              'ml.m6i.xlarge',
                                                              'ml.p4de.24xlarge'}},
 'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                                                                     'ml.c6i.16xlarge',
                                                                                                                                     'ml.c6i.24xlarge',
                                                                                                                                     'ml.c6i.2xlarge',
                                                                                                                                     'ml.c6i.32xlarge',
                                                                                                                                     'ml.c6i.4xlarge',
                                                                                                                                     'ml.c6i.8xlarge',
                                                                                                                                     'ml.c6i.xlarge',
                                                                                                                                     'ml.m6i.12xlarge',
                                                                                                                                     'ml.m6i.16xlarge',
                                                                                                                                     'ml.m6i.24xlarge',
                                                                                                                                     'ml.m6i.2xlarge',
                                                                                                                                     'ml.m6i.32xlarge',
                                                                                                                                     'ml.m6i.4xlarge',
                                                                                                                                     'ml.m6i.8xlarge',
                                                                                                                                     'ml.m6i.large',
                                                                                                                                     'ml.m6i.xlarge',
                                                                                                                                     'ml.p4de.24xlarge'}},
                                                                                                 'InstanceType': {'ml.c6i.12xlarge',
                                                                                                                  'ml.c6i.16xlarge',
                                                                                                                  'ml.c6i.24xlarge',
                                                                                                                  'ml.c6i.2xlarge',
                                                                                                                  'ml.c6i.32xlarge',
                                                                                                                  'ml.c6i.4xlarge',
                                                                                                                  'ml.c6i.8xlarge',
                                                                                                                  'ml.c6i.xlarge',
                                                                                                                  'ml.m6i.12xlarge',
                                                                                                                  'ml.m6i.16xlarge',
                                                                                                                  'ml.m6i.24xlarge',
                                                                                                                  'ml.m6i.2xlarge',
                                                                                                                  'ml.m6i.32xlarge',
                                                                                                                  'ml.m6i.4xlarge',
                                                                                                                  'ml.m6i.8xlarge',
                                                                                                                  'ml.m6i.large',
                                                                                                                  'ml.m6i.xlarge',
                                                                                                                  'ml.p4de.24xlarge'}}}}}}

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

See also: AWS API Documentation

Request Syntax

client.create_algorithm(
    AlgorithmName='string',
    AlgorithmDescription='string',
    TrainingSpecification={
        'TrainingImage': 'string',
        'TrainingImageDigest': 'string',
        'SupportedHyperParameters': [
            {
                'Name': 'string',
                'Description': 'string',
                'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
                'Range': {
                    'IntegerParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'ContinuousParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'CategoricalParameterRangeSpecification': {
                        'Values': [
                            'string',
                        ]
                    }
                },
                'IsTunable': True|False,
                'IsRequired': True|False,
                'DefaultValue': 'string'
            },
        ],
        'SupportedTrainingInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
        ],
        'SupportsDistributedTraining': True|False,
        'MetricDefinitions': [
            {
                'Name': 'string',
                'Regex': 'string'
            },
        ],
        'TrainingChannels': [
            {
                'Name': 'string',
                'Description': 'string',
                'IsRequired': True|False,
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedCompressionTypes': [
                    'None'|'Gzip',
                ],
                'SupportedInputModes': [
                    'Pipe'|'File'|'FastFile',
                ]
            },
        ],
        'SupportedTuningJobObjectiveMetrics': [
            {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'string'
            },
        ],
        'AdditionalS3DataSource': {
            'S3DataType': 'S3Object',
            'S3Uri': 'string',
            'CompressionType': 'None'|'Gzip'
        }
    },
    InferenceSpecification={
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    ValidationSpecification={
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TrainingJobDefinition': {
                    'TrainingInputMode': 'Pipe'|'File'|'FastFile',
                    'HyperParameters': {
                        'string': 'string'
                    },
                    'InputDataConfig': [
                        {
                            'ChannelName': 'string',
                            'DataSource': {
                                'S3DataSource': {
                                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                    'S3Uri': 'string',
                                    'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                                    'AttributeNames': [
                                        'string',
                                    ],
                                    'InstanceGroupNames': [
                                        'string',
                                    ]
                                },
                                'FileSystemDataSource': {
                                    'FileSystemId': 'string',
                                    'FileSystemAccessMode': 'rw'|'ro',
                                    'FileSystemType': 'EFS'|'FSxLustre',
                                    'DirectoryPath': 'string'
                                }
                            },
                            'ContentType': 'string',
                            'CompressionType': 'None'|'Gzip',
                            'RecordWrapperType': 'None'|'RecordIO',
                            'InputMode': 'Pipe'|'File'|'FastFile',
                            'ShuffleConfig': {
                                'Seed': 123
                            }
                        },
                    ],
                    'OutputDataConfig': {
                        'KmsKeyId': 'string',
                        'S3OutputPath': 'string',
                        'CompressionType': 'GZIP'|'NONE'
                    },
                    'ResourceConfig': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123,
                        'VolumeKmsKeyId': 'string',
                        'KeepAlivePeriodInSeconds': 123,
                        'InstanceGroups': [
                            {
                                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                                'InstanceCount': 123,
                                'InstanceGroupName': 'string'
                            },
                        ]
                    },
                    'StoppingCondition': {
                        'MaxRuntimeInSeconds': 123,
                        'MaxWaitTimeInSeconds': 123,
                        'MaxPendingTimeInSeconds': 123
                    }
                },
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    CertifyForMarketplace=True|False,
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type AlgorithmName

string

param AlgorithmName

[REQUIRED]

The name of the algorithm.

type AlgorithmDescription

string

param AlgorithmDescription

A description of the algorithm.

type TrainingSpecification

dict

param TrainingSpecification

[REQUIRED]

Specifies details about training jobs run by this algorithm, including the following:

  • The Amazon ECR path of the container and the version digest of the algorithm.

  • The hyperparameters that the algorithm supports.

  • The instance types that the algorithm supports for training.

  • Whether the algorithm supports distributed training.

  • The metrics that the algorithm emits to Amazon CloudWatch.

  • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

  • The input channels that the algorithm supports for training data. For example, an algorithm might support train , validation , and test channels.

  • TrainingImage (string) -- [REQUIRED]

    The Amazon ECR registry path of the Docker image that contains the training algorithm.

  • TrainingImageDigest (string) --

    An MD5 hash of the training algorithm that identifies the Docker image used for training.

  • SupportedHyperParameters (list) --

    A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

    • (dict) --

      Defines a hyperparameter to be used by an algorithm.

      • Name (string) -- [REQUIRED]

        The name of this hyperparameter. The name must be unique.

      • Description (string) --

        A brief description of the hyperparameter.

      • Type (string) -- [REQUIRED]

        The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .

      • Range (dict) --

        The allowed range for this hyperparameter.

        • IntegerParameterRangeSpecification (dict) --

          A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

          • MinValue (string) -- [REQUIRED]

            The minimum integer value allowed.

          • MaxValue (string) -- [REQUIRED]

            The maximum integer value allowed.

        • ContinuousParameterRangeSpecification (dict) --

          A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

          • MinValue (string) -- [REQUIRED]

            The minimum floating-point value allowed.

          • MaxValue (string) -- [REQUIRED]

            The maximum floating-point value allowed.

        • CategoricalParameterRangeSpecification (dict) --

          A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

          • Values (list) -- [REQUIRED]

            The allowed categories for the hyperparameter.

            • (string) --

      • IsTunable (boolean) --

        Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

      • IsRequired (boolean) --

        Indicates whether this hyperparameter is required.

      • DefaultValue (string) --

        The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

  • SupportedTrainingInstanceTypes (list) -- [REQUIRED]

    A list of the instance types that this algorithm can use for training.

    • (string) --

  • SupportsDistributedTraining (boolean) --

    Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

  • MetricDefinitions (list) --

    A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

    • (dict) --

      Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

      • Name (string) -- [REQUIRED]

        The name of the metric.

      • Regex (string) -- [REQUIRED]

        A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

  • TrainingChannels (list) -- [REQUIRED]

    A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

    • (dict) --

      Defines a named input source, called a channel, to be used by an algorithm.

      • Name (string) -- [REQUIRED]

        The name of the channel.

      • Description (string) --

        A brief description of the channel.

      • IsRequired (boolean) --

        Indicates whether the channel is required by the algorithm.

      • SupportedContentTypes (list) -- [REQUIRED]

        The supported MIME types for the data.

        • (string) --

      • SupportedCompressionTypes (list) --

        The allowed compression types, if data compression is used.

        • (string) --

      • SupportedInputModes (list) -- [REQUIRED]

        The allowed input mode, either FILE or PIPE.

        In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

        In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

        • (string) --

          The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

          Pipe mode

          If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

          File mode

          If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

          You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

          For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

          FastFile mode

          If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

          FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

  • SupportedTuningJobObjectiveMetrics (list) --

    A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

    • (dict) --

      Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .

      • Type (string) -- [REQUIRED]

        Whether to minimize or maximize the objective metric.

      • MetricName (string) -- [REQUIRED]

        The name of the metric to use for the objective metric.

  • AdditionalS3DataSource (dict) --

    The additional data source used during the training job.

    • S3DataType (string) -- [REQUIRED]

      The data type of the additional data source that you specify for use in inference or training.

    • S3Uri (string) -- [REQUIRED]

      The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

    • CompressionType (string) --

      The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

type InferenceSpecification

dict

param InferenceSpecification

Specifies details about inference jobs that the algorithm runs, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the algorithm supports for inference.

  • Containers (list) -- [REQUIRED]

    The Amazon ECR registry path of the Docker image that contains the inference code.

    • (dict) --

      Describes the Docker container for the model package.

      • ContainerHostname (string) --

        The DNS host name for the Docker container.

      • Image (string) -- [REQUIRED]

        The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

        If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

      • ImageDigest (string) --

        An MD5 hash of the training algorithm that identifies the Docker image used for training.

      • ModelDataUrl (string) --

        The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

        Note

        The model artifacts must be in an S3 bucket that is in the same region as the model package.

      • ProductId (string) --

        The Amazon Web Services Marketplace product ID of the model package.

      • Environment (dict) --

        The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

        • (string) --

          • (string) --

      • ModelInput (dict) --

        A structure with Model Input details.

        • DataInputConfig (string) -- [REQUIRED]

          The input configuration object for the model.

      • Framework (string) --

        The machine learning framework of the model package container image.

      • FrameworkVersion (string) --

        The framework version of the Model Package Container Image.

      • NearestModelName (string) --

        The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

      • AdditionalS3DataSource (dict) --

        The additional data source that is used during inference in the Docker container for your model package.

        • S3DataType (string) -- [REQUIRED]

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) -- [REQUIRED]

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

  • SupportedTransformInstanceTypes (list) --

    A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

    This parameter is required for unversioned models, and optional for versioned models.

    • (string) --

  • SupportedRealtimeInferenceInstanceTypes (list) --

    A list of the instance types that are used to generate inferences in real-time.

    This parameter is required for unversioned models, and optional for versioned models.

    • (string) --

  • SupportedContentTypes (list) --

    The supported MIME types for the input data.

    • (string) --

  • SupportedResponseMIMETypes (list) --

    The supported MIME types for the output data.

    • (string) --

type ValidationSpecification

dict

param ValidationSpecification

Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.

  • ValidationRole (string) -- [REQUIRED]

    The IAM roles that SageMaker uses to run the training jobs.

  • ValidationProfiles (list) -- [REQUIRED]

    An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

    • (dict) --

      Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.

      The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

      • ProfileName (string) -- [REQUIRED]

        The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

      • TrainingJobDefinition (dict) -- [REQUIRED]

        The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

        • TrainingInputMode (string) -- [REQUIRED]

          The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

          Pipe mode

          If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

          File mode

          If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

          You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

          For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

          FastFile mode

          If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

          FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

        • HyperParameters (dict) --

          The hyperparameters used for the training job.

          • (string) --

            • (string) --

        • InputDataConfig (list) -- [REQUIRED]

          An array of Channel objects, each of which specifies an input source.

          • (dict) --

            A channel is a named input source that training algorithms can consume.

            • ChannelName (string) -- [REQUIRED]

              The name of the channel.

            • DataSource (dict) -- [REQUIRED]

              The location of the channel data.

              • S3DataSource (dict) --

                The S3 location of the data source that is associated with a channel.

                • S3DataType (string) -- [REQUIRED]

                  If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

                  If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

                  If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

                • S3Uri (string) -- [REQUIRED]

                  Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                  • A key name prefix might look like this: s3://bucketname/exampleprefix

                  • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

                  Your input bucket must be located in same Amazon Web Services region as your training job.

                • S3DataDistributionType (string) --

                  If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

                  If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

                  Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

                  In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

                • AttributeNames (list) --

                  A list of one or more attribute names to use that are found in a specified augmented manifest file.

                  • (string) --

                • InstanceGroupNames (list) --

                  A list of names of instance groups that get data from the S3 data source.

                  • (string) --

              • FileSystemDataSource (dict) --

                The file system that is associated with a channel.

                • FileSystemId (string) -- [REQUIRED]

                  The file system id.

                • FileSystemAccessMode (string) -- [REQUIRED]

                  The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

                • FileSystemType (string) -- [REQUIRED]

                  The file system type.

                • DirectoryPath (string) -- [REQUIRED]

                  The full path to the directory to associate with the channel.

            • ContentType (string) --

              The MIME type of the data.

            • CompressionType (string) --

              If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

            • RecordWrapperType (string) --

              Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .

              In File mode, leave this field unset or set it to None.

            • InputMode (string) --

              (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

              To use a model for incremental training, choose File input model.

            • ShuffleConfig (dict) --

              A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

              For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

              • Seed (integer) -- [REQUIRED]

                Determines the shuffling order in ShuffleConfig value.

        • OutputDataConfig (dict) -- [REQUIRED]

          the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

            • // KMS Key Alias "alias/ExampleAlias"

            • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

            If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

            The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

          • S3OutputPath (string) -- [REQUIRED]

            Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

          • CompressionType (string) --

            The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

        • ResourceConfig (dict) -- [REQUIRED]

          The resources, including the ML compute instances and ML storage volumes, to use for model training.

          • InstanceType (string) --

            The ML compute instance type.

            Note

            SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

            Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

            • US East (N. Virginia) (us-east-1)

            • US West (Oregon) (us-west-2)

            To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

          • InstanceCount (integer) --

            The number of ML compute instances to use. For distributed training, provide a value greater than 1.

          • VolumeSizeInGB (integer) -- [REQUIRED]

            The size of the ML storage volume that you want to provision.

            ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

            When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

            When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

            To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .

            To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .

          • VolumeKmsKeyId (string) --

            The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

            Note

            Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

            For a list of instance types that support local instance storage, see Instance Store Volumes .

            For more information about local instance storage encryption, see SSD Instance Store Volumes .

            The VolumeKmsKeyId can be in any of the following formats:

            • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

          • KeepAlivePeriodInSeconds (integer) --

            The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

          • InstanceGroups (list) --

            The configuration of a heterogeneous cluster in JSON format.

            • (dict) --

              Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

              • InstanceType (string) -- [REQUIRED]

                Specifies the instance type of the instance group.

              • InstanceCount (integer) -- [REQUIRED]

                Specifies the number of instances of the instance group.

              • InstanceGroupName (string) -- [REQUIRED]

                Specifies the name of the instance group.

        • StoppingCondition (dict) -- [REQUIRED]

          Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

          To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

          • MaxRuntimeInSeconds (integer) --

            The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

            For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

            For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

            The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

          • MaxWaitTimeInSeconds (integer) --

            The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

            When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

          • MaxPendingTimeInSeconds (integer) --

            The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

      • TransformJobDefinition (dict) --

        The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

        • MaxConcurrentTransforms (integer) --

          The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

        • MaxPayloadInMB (integer) --

          The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

        • BatchStrategy (string) --

          A string that determines the number of records included in a single mini-batch.

          SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

        • Environment (dict) --

          The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

          • (string) --

            • (string) --

        • TransformInput (dict) -- [REQUIRED]

          A description of the input source and the way the transform job consumes it.

          • DataSource (dict) -- [REQUIRED]

            Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

            • S3DataSource (dict) -- [REQUIRED]

              The S3 location of the data source that is associated with a channel.

              • S3DataType (string) -- [REQUIRED]

                If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

                If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

                The following values are compatible: ManifestFile , S3Prefix

                The following value is not compatible: AugmentedManifestFile

              • S3Uri (string) -- [REQUIRED]

                Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                • A key name prefix might look like this: s3://bucketname/exampleprefix .

                • A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

          • ContentType (string) --

            The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

          • CompressionType (string) --

            If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .

          • SplitType (string) --

            The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:

            • RecordIO

            • TFRecord

            When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.

            Note

            Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .

            For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.

        • TransformOutput (dict) -- [REQUIRED]

          Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

          • S3OutputPath (string) -- [REQUIRED]

            The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .

            For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

          • Accept (string) --

            The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

          • AssembleWith (string) --

            Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

            If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

            The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • TransformResources (dict) -- [REQUIRED]

          Identifies the ML compute instances for the transform job.

          • InstanceType (string) -- [REQUIRED]

            The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.

          • InstanceCount (integer) -- [REQUIRED]

            The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .

          • VolumeKmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.

            Note

            Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

            For a list of instance types that support local instance storage, see Instance Store Volumes .

            For more information about local instance storage encryption, see SSD Instance Store Volumes .

            The VolumeKmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

type CertifyForMarketplace

boolean

param CertifyForMarketplace

Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'AlgorithmArn': 'string'
}

Response Structure

  • (dict) --

    • AlgorithmArn (string) --

      The Amazon Resource Name (ARN) of the new algorithm.

CreateApp (updated) Link ¶
Changes (request)
{'AppType': {'Canvas',
             'DatasetManager',
             'DetailedProfiler',
             'Local',
             'RSession',
             'SageMakerLite',
             'Savitur',
             'VSCode'}}

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

See also: AWS API Documentation

Request Syntax

client.create_app(
    DomainId='string',
    UserProfileName='string',
    SpaceName='string',
    AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
    AppName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ResourceSpec={
        'SageMakerImageArn': 'string',
        'SageMakerImageVersionArn': 'string',
        'SageMakerImageVersionAlias': 'string',
        'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
        'LifecycleConfigArn': 'string'
    }
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

The user profile name. If this value is not set, then SpaceName must be set.

type SpaceName

string

param SpaceName

The name of the space. If this value is not set, then UserProfileName must be set.

type AppType

string

param AppType

[REQUIRED]

The type of app.

type AppName

string

param AppName

[REQUIRED]

The name of the app.

type Tags

list

param Tags

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type ResourceSpec

dict

param ResourceSpec

The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

Note

The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

  • SageMakerImageArn (string) --

    The ARN of the SageMaker image that the image version belongs to.

  • SageMakerImageVersionArn (string) --

    The ARN of the image version created on the instance.

  • SageMakerImageVersionAlias (string) --

    The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

  • InstanceType (string) --

    The instance type that the image version runs on.

    Note

    JupyterServer apps only support the system value.

    For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

  • LifecycleConfigArn (string) --

    The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

rtype

dict

returns

Response Syntax

{
    'AppArn': 'string'
}

Response Structure

  • (dict) --

    • AppArn (string) --

      The Amazon Resource Name (ARN) of the app.

CreateAppImageConfig (updated) Link ¶
Changes (request)
{'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
                                                   'DefaultUid': 'integer',
                                                   'MountPath': 'string'}}}

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

See also: AWS API Documentation

Request Syntax

client.create_app_image_config(
    AppImageConfigName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    KernelGatewayImageConfig={
        'KernelSpecs': [
            {
                'Name': 'string',
                'DisplayName': 'string'
            },
        ],
        'FileSystemConfig': {
            'MountPath': 'string',
            'DefaultUid': 123,
            'DefaultGid': 123
        }
    },
    JupyterLabAppImageConfig={
        'FileSystemConfig': {
            'MountPath': 'string',
            'DefaultUid': 123,
            'DefaultGid': 123
        },
        'ContainerConfig': {
            'ContainerArguments': [
                'string',
            ],
            'ContainerEntrypoint': [
                'string',
            ],
            'ContainerEnvironmentVariables': {
                'string': 'string'
            }
        }
    }
)
type AppImageConfigName

string

param AppImageConfigName

[REQUIRED]

The name of the AppImageConfig. Must be unique to your account.

type Tags

list

param Tags

A list of tags to apply to the AppImageConfig.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type KernelGatewayImageConfig

dict

param KernelGatewayImageConfig

The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

  • KernelSpecs (list) -- [REQUIRED]

    The specification of the Jupyter kernels in the image.

    • (dict) --

      The specification of a Jupyter kernel.

      • Name (string) -- [REQUIRED]

        The name of the Jupyter kernel in the image. This value is case sensitive.

      • DisplayName (string) --

        The display name of the kernel.

  • FileSystemConfig (dict) --

    The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

    • MountPath (string) --

      The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .

    • DefaultUid (integer) --

      The default POSIX user ID (UID). If not specified, defaults to 1000 .

    • DefaultGid (integer) --

      The default POSIX group ID (GID). If not specified, defaults to 100 .

type JupyterLabAppImageConfig

dict

param JupyterLabAppImageConfig

The JupyterLabAppImageConfig . You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

  • FileSystemConfig (dict) --

    The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

    • MountPath (string) --

      The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .

    • DefaultUid (integer) --

      The default POSIX user ID (UID). If not specified, defaults to 1000 .

    • DefaultGid (integer) --

      The default POSIX group ID (GID). If not specified, defaults to 100 .

  • ContainerConfig (dict) --

    The configuration used to run the application image container.

    • ContainerArguments (list) --

      The arguments for the container when you're running the application.

      • (string) --

    • ContainerEntrypoint (list) --

      The entrypoint used to run the application in the container.

      • (string) --

    • ContainerEnvironmentVariables (dict) --

      The environment variables to set in the container

      • (string) --

        • (string) --

rtype

dict

returns

Response Syntax

{
    'AppImageConfigArn': 'string'
}

Response Structure

  • (dict) --

    • AppImageConfigArn (string) --

      The Amazon Resource Name (ARN) of the AppImageConfig.

CreateAutoMLJobV2 (updated) Link ¶
Changes (request)
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'ModelAccessConfig': {'AcceptEula': 'boolean'}}}}

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

Note

CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob , as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2 .

For the list of available problem types supported by CreateAutoMLJobV2 , see AutoMLProblemTypeConfig .

You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2 .

See also: AWS API Documentation

Request Syntax

client.create_auto_ml_job_v2(
    AutoMLJobName='string',
    AutoMLJobInputDataConfig=[
        {
            'ChannelType': 'training'|'validation',
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            }
        },
    ],
    OutputDataConfig={
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    AutoMLProblemTypeConfig={
        'ImageClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            }
        },
        'TextClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ContentColumn': 'string',
            'TargetLabelColumn': 'string'
        },
        'TimeSeriesForecastingJobConfig': {
            'FeatureSpecificationS3Uri': 'string',
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ForecastFrequency': 'string',
            'ForecastHorizon': 123,
            'ForecastQuantiles': [
                'string',
            ],
            'Transformations': {
                'Filling': {
                    'string': {
                        'string': 'string'
                    }
                },
                'Aggregation': {
                    'string': 'sum'|'avg'|'first'|'min'|'max'
                }
            },
            'TimeSeriesConfig': {
                'TargetAttributeName': 'string',
                'TimestampAttributeName': 'string',
                'ItemIdentifierAttributeName': 'string',
                'GroupingAttributeNames': [
                    'string',
                ]
            },
            'HolidayConfig': [
                {
                    'CountryCode': 'string'
                },
            ]
        },
        'TabularJobConfig': {
            'CandidateGenerationConfig': {
                'AlgorithmsConfig': [
                    {
                        'AutoMLAlgorithms': [
                            'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
                        ]
                    },
                ]
            },
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'FeatureSpecificationS3Uri': 'string',
            'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
            'GenerateCandidateDefinitionsOnly': True|False,
            'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
            'TargetAttributeName': 'string',
            'SampleWeightAttributeName': 'string'
        },
        'TextGenerationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'BaseModelName': 'string',
            'TextGenerationHyperParameters': {
                'string': 'string'
            },
            'ModelAccessConfig': {
                'AcceptEula': True|False
            }
        }
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    SecurityConfig={
        'VolumeKmsKeyId': 'string',
        'EnableInterContainerTrafficEncryption': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    AutoMLJobObjective={
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
    },
    ModelDeployConfig={
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    DataSplitConfig={
        'ValidationFraction': ...
    }
)
type AutoMLJobName

string

param AutoMLJobName

[REQUIRED]

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

type AutoMLJobInputDataConfig

list

param AutoMLJobInputDataConfig

[REQUIRED]

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

  • For tabular problem types: S3Prefix , ManifestFile .

  • For image classification: S3Prefix , ManifestFile , AugmentedManifestFile .

  • For text classification: S3Prefix .

  • For time-series forecasting: S3Prefix .

  • For text generation (LLMs fine-tuning): S3Prefix .

  • (dict) --

    A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).

    • ChannelType (string) --

      The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType

      Note

      The type of channel defaults to training for the time-series forecasting problem type.

    • ContentType (string) --

      The content type of the data from the input source. The following are the allowed content types for different problems:

      • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For image classification: image/png , image/jpeg , or image/* . The default value is image/* .

      • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

    • CompressionType (string) --

      The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .

    • DataSource (dict) --

      The data source for an AutoML channel (Required).

      • S3DataSource (dict) -- [REQUIRED]

        The Amazon S3 location of the input data.

        • S3DataType (string) -- [REQUIRED]

          The data type.

          • If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

          • If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

          • If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .

        • S3Uri (string) -- [REQUIRED]

          The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

type OutputDataConfig

dict

param OutputDataConfig

[REQUIRED]

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

  • KmsKeyId (string) --

    The Key Management Service (KMS) encryption key ID.

  • S3OutputPath (string) -- [REQUIRED]

    The Amazon S3 output path. Must be 128 characters or less.

type AutoMLProblemTypeConfig

dict

param AutoMLProblemTypeConfig

[REQUIRED]

Defines the configuration settings of one of the supported problem types.

  • ImageClassificationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the image classification problem type.

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

  • TextClassificationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the text classification problem type.

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • ContentColumn (string) -- [REQUIRED]

      The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

    • TargetLabelColumn (string) -- [REQUIRED]

      The name of the column used to provide the class labels. It should not be same as the content column.

  • TimeSeriesForecastingJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

    • FeatureSpecificationS3Uri (string) --

      A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .

      You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] } .

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      Autopilot supports the following data types: numeric , categorical , text , and datetime .

      Note

      These column keys must not include any column set in TimeSeriesConfig .

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • ForecastFrequency (string) -- [REQUIRED]

      The frequency of predictions in a forecast.

      Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .

      The valid values for each frequency are the following:

      • Minute - 1-59

      • Hour - 1-23

      • Day - 1-6

      • Week - 1-4

      • Month - 1-11

      • Year - 1

    • ForecastHorizon (integer) -- [REQUIRED]

      The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

    • ForecastQuantiles (list) --

      The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

      • (string) --

    • Transformations (dict) --

      The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

      • Filling (dict) --

        A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.

        The supported filling methods and their corresponding options are:

        • frontfill : none (Supported only for target column)

        • middlefill : zero , value , median , mean , min , max

        • backfill : zero , value , median , mean , min , max

        • futurefill : zero , value , median , mean , min , max

        To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .

        • (string) --

          • (dict) --

            • (string) --

              • (string) --

      • Aggregation (dict) --

        A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.

        The supported aggregation methods are sum (default), avg , first , min , max .

        Note

        Aggregation is only supported for the target column.

        • (string) --

          • (string) --

    • TimeSeriesConfig (dict) -- [REQUIRED]

      The collection of components that defines the time-series.

      • TargetAttributeName (string) -- [REQUIRED]

        The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.

      • TimestampAttributeName (string) -- [REQUIRED]

        The name of the column indicating a point in time at which the target value of a given item is recorded.

      • ItemIdentifierAttributeName (string) -- [REQUIRED]

        The name of the column that represents the set of item identifiers for which you want to predict the target value.

      • GroupingAttributeNames (list) --

        A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.

        • (string) --

    • HolidayConfig (list) --

      The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

      • (dict) --

        Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

        • CountryCode (string) --

          The country code for the holiday calendar.

          For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.

  • TabularJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

    • CandidateGenerationConfig (dict) --

      The configuration information of how model candidates are generated.

      • AlgorithmsConfig (list) --

        Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.

        The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .

        • AlgorithmsConfig should not be set in AUTO training mode.

        • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

        For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .

        For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

        • (dict) --

          The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.

          • AutoMLAlgorithms (list) -- [REQUIRED]

            The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

            Note

            Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.

            • In ENSEMBLING mode:

              • "catboost"

              • "extra-trees"

              • "fastai"

              • "lightgbm"

              • "linear-learner"

              • "nn-torch"

              • "randomforest"

              • "xgboost"

            • In HYPERPARAMETER_TUNING mode:

              • "linear-learner"

              • "mlp"

              • "xgboost"

            • (string) --

    • CompletionCriteria (dict) --

      How long a job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • FeatureSpecificationS3Uri (string) --

      A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] } .

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      Note

      These column keys may not include the target column.

      In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .

      If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

      If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

      The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

    • Mode (string) --

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

    • GenerateCandidateDefinitionsOnly (boolean) --

      Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    • ProblemType (string) --

      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .

      Note

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

    • TargetAttributeName (string) -- [REQUIRED]

      The name of the target variable in supervised learning, usually represented by 'y'.

    • SampleWeightAttributeName (string) --

      If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .

      Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

      Support for sample weights is available in Ensembling mode only.

  • TextGenerationJobConfig (dict) --

    Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.

    Note

    The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .

    • CompletionCriteria (dict) --

      How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

        For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

        For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

        For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

    • BaseModelName (string) --

      The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .

    • TextGenerationHyperParameters (dict) --

      The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .

      • "epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

      • "batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

      • "learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

      • "learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

      Here is an example where all four hyperparameters are configured.

      { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

      • (string) --

        • (string) --

    • ModelAccessConfig (dict) --

      The access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . For more information, see End-user license agreements .

      • AcceptEula (boolean) -- [REQUIRED]

        Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

type RoleArn

string

param RoleArn

[REQUIRED]

The ARN of the role that is used to access the data.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources . Tag keys must be unique per resource.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type SecurityConfig

dict

param SecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

  • VolumeKmsKeyId (string) --

    The key used to encrypt stored data.

  • EnableInterContainerTrafficEncryption (boolean) --

    Whether to use traffic encryption between the container layers.

  • VpcConfig (dict) --

    The VPC configuration.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

      • (string) --

type AutoMLJobObjective

dict

param AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective .

Note

  • For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType ), or none at all.

  • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

  • MetricName (string) -- [REQUIRED]

    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

    The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

    • For tabular problem types:

      • List of available metrics:

        • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

        • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

        • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

      For a description of each metric, see Autopilot metrics for classification and regression .

      • Default objective metrics:

        • Regression: MSE .

        • Binary classification: F1 .

        • Multiclass classification: Accuracy .

    • For image or text classification problem types:

    • For time-series forecasting problem types:

    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

type ModelDeployConfig

dict

param ModelDeployConfig

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • AutoGenerateEndpointName (boolean) --

    Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

    Note

    If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

  • EndpointName (string) --

    Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

    Note

    Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

type DataSplitConfig

dict

param DataSplitConfig

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob , the validation dataset must be less than 2 GB in size.

Note

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

  • ValidationFraction (float) --

    The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

rtype

dict

returns

Response Syntax

{
    'AutoMLJobArn': 'string'
}

Response Structure

  • (dict) --

    • AutoMLJobArn (string) --

      The unique ARN assigned to the AutoMLJob when it is created.

CreateCompilationJob (updated) Link ¶
Changes (request)
{'OutputConfig': {'TargetDevice': {'ml_c6g', 'ml_m6g', 'rasp4b'}}}

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

  • A name for the compilation job

  • Information about the input model artifacts

  • The output location for the compiled model and the device (target) that the model runs on

  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob . To get information about a particular model compilation job, use DescribeCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .

See also: AWS API Documentation

Request Syntax

client.create_compilation_job(
    CompilationJobName='string',
    RoleArn='string',
    ModelPackageVersionArn='string',
    InputConfig={
        'S3Uri': 'string',
        'DataInputConfig': 'string',
        'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
        'FrameworkVersion': 'string'
    },
    OutputConfig={
        'S3OutputLocation': 'string',
        'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
        'TargetPlatform': {
            'Os': 'ANDROID'|'LINUX',
            'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
            'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA'
        },
        'CompilerOptions': 'string',
        'KmsKeyId': 'string'
    },
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123,
        'MaxPendingTimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type CompilationJobName

string

param CompilationJobName

[REQUIRED]

A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

During model compilation, Amazon SageMaker needs your permission to:

  • Read input data from an S3 bucket

  • Write model artifacts to an S3 bucket

  • Write logs to Amazon CloudWatch Logs

  • Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

type ModelPackageVersionArn

string

param ModelPackageVersionArn

The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

type InputConfig

dict

param InputConfig

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

  • S3Uri (string) -- [REQUIRED]

    The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

  • DataInputConfig (string) --

    Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.

    • TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

      • Examples for one input:

        • If using the console, {"input":[1,1024,1024,3]}

        • If using the CLI, {\"input\":[1,1024,1024,3]}

      • Examples for two inputs:

        • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

        • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}

    • KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.

      • Examples for one input:

        • If using the console, {"input_1":[1,3,224,224]}

        • If using the CLI, {\"input_1\":[1,3,224,224]}

      • Examples for two inputs:

        • If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

        • If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}

    • MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

      • Examples for one input:

        • If using the console, {"data":[1,3,1024,1024]}

        • If using the CLI, {\"data\":[1,3,1024,1024]}

      • Examples for two inputs:

        • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

        • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}

    • PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

      • Examples for one input in dictionary format:

        • If using the console, {"input0":[1,3,224,224]}

        • If using the CLI, {\"input0\":[1,3,224,224]}

      • Example for one input in list format: [[1,3,224,224]]

      • Examples for two inputs in dictionary format:

        • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

        • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}

      • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

    • XGBOOST : input data name and shape are not needed.

    DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):

    • shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:

      • Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}

      • Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

    • default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}

    • type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .

    • bias : If the input type is an Image, you need to provide the bias vector.

    • scale : If the input type is an Image, you need to provide a scale factor.

    CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:

    • Tensor type input:

      • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}

    • Tensor type input without input name (PyTorch):

      • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]

    • Image type input:

      • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}

      • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

    • Image type input without input name (PyTorch):

      • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]

      • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

    Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .

  • Framework (string) -- [REQUIRED]

    Identifies the framework in which the model was trained. For example: TENSORFLOW.

  • FrameworkVersion (string) --

    Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

    For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks .

type OutputConfig

dict

param OutputConfig

[REQUIRED]

Provides information about the output location for the compiled model and the target device the model runs on.

  • S3OutputLocation (string) -- [REQUIRED]

    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

  • TargetDevice (string) --

    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .

    Note

    Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

  • TargetPlatform (dict) --

    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .

    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

    • Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}

    • Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

    • EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}

    • RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

    • ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

    • ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}

    • Os (string) -- [REQUIRED]

      Specifies a target platform OS.

      • LINUX : Linux-based operating systems.

      • ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}

    • Arch (string) -- [REQUIRED]

      Specifies a target platform architecture.

      • X86_64 : 64-bit version of the x86 instruction set.

      • X86 : 32-bit version of the x86 instruction set.

      • ARM64 : ARMv8 64-bit CPU.

      • ARM_EABIHF : ARMv7 32-bit, Hard Float.

      • ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.

    • Accelerator (string) --

      Specifies a target platform accelerator (optional).

      • NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options

      • MALI : ARM Mali graphics processor

      • INTEL_GRAPHICS : Integrated Intel graphics

  • CompilerOptions (string) --

    Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

    • DTYPE : Specifies the data type for the input. When compiling for ml_* (except for ml_inf ) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

      • float32: Use either "float" or "float32" .

      • int64: Use either "int64" or "long" .

    For example, {"dtype" : "float32"} .

    • CPU : Compilation for CPU supports the following compiler options.

      • mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

      • mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

    • ARM : Details of ARM CPU compilations.

      • NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

    • NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.

      • gpu_code : Specifies the targeted architecture.

      • trt-ver : Specifies the TensorRT versions in x.y.z. format.

      • cuda-ver : Specifies the CUDA version in x.y format.

    For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

    • ANDROID : Compilation for the Android OS supports the following compiler options:

      • ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .

      • mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

    • INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI Reference Guide .

    • CoreML : Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:

      • class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.

    • EIA : Compilation for the Elastic Inference Accelerator supports the following compiler options:

      • precision_mode : Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32" . Default is "FP32" .

      • signature_def_key : Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.

      • output_names : Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names .

    For example: {"precision_mode": "FP32", "output_names": ["output:0"]}

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

    The KmsKeyId can be any of the following formats:

    • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

    • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

    • Alias name: alias/ExampleAlias

    • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

type VpcConfig

dict

param VpcConfig

A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .

  • SecurityGroupIds (list) -- [REQUIRED]

    The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

    • (string) --

  • Subnets (list) -- [REQUIRED]

    The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.

    • (string) --

type StoppingCondition

dict

param StoppingCondition

[REQUIRED]

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

  • MaxRuntimeInSeconds (integer) --

    The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

    For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

    For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

    The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

  • MaxWaitTimeInSeconds (integer) --

    The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

    When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

  • MaxPendingTimeInSeconds (integer) --

    The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'CompilationJobArn': 'string'
}

Response Structure

  • (dict) --

    • CompilationJobArn (string) --

      If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

      • CompilationJobArn : The Amazon Resource Name (ARN) of the compiled job.

CreateEndpointConfig (updated) Link ¶
Changes (request)
{'DataCaptureConfig': {'CaptureOptions': {'CaptureMode': {'InputAndOutput'}}},
 'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                         'ml.c7i.16xlarge',
                                         'ml.c7i.24xlarge',
                                         'ml.c7i.2xlarge',
                                         'ml.c7i.48xlarge',
                                         'ml.c7i.4xlarge',
                                         'ml.c7i.8xlarge',
                                         'ml.c7i.large',
                                         'ml.c7i.xlarge',
                                         'ml.dl1.24xlarge',
                                         'ml.m7i.12xlarge',
                                         'ml.m7i.16xlarge',
                                         'ml.m7i.24xlarge',
                                         'ml.m7i.2xlarge',
                                         'ml.m7i.48xlarge',
                                         'ml.m7i.4xlarge',
                                         'ml.m7i.8xlarge',
                                         'ml.m7i.large',
                                         'ml.m7i.xlarge',
                                         'ml.r7i.12xlarge',
                                         'ml.r7i.16xlarge',
                                         'ml.r7i.24xlarge',
                                         'ml.r7i.2xlarge',
                                         'ml.r7i.48xlarge',
                                         'ml.r7i.4xlarge',
                                         'ml.r7i.8xlarge',
                                         'ml.r7i.large',
                                         'ml.r7i.xlarge',
                                         'ml.trn1n.32xlarge'}},
 'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                               'ml.c7i.16xlarge',
                                               'ml.c7i.24xlarge',
                                               'ml.c7i.2xlarge',
                                               'ml.c7i.48xlarge',
                                               'ml.c7i.4xlarge',
                                               'ml.c7i.8xlarge',
                                               'ml.c7i.large',
                                               'ml.c7i.xlarge',
                                               'ml.dl1.24xlarge',
                                               'ml.m7i.12xlarge',
                                               'ml.m7i.16xlarge',
                                               'ml.m7i.24xlarge',
                                               'ml.m7i.2xlarge',
                                               'ml.m7i.48xlarge',
                                               'ml.m7i.4xlarge',
                                               'ml.m7i.8xlarge',
                                               'ml.m7i.large',
                                               'ml.m7i.xlarge',
                                               'ml.r7i.12xlarge',
                                               'ml.r7i.16xlarge',
                                               'ml.r7i.24xlarge',
                                               'ml.r7i.2xlarge',
                                               'ml.r7i.48xlarge',
                                               'ml.r7i.4xlarge',
                                               'ml.r7i.8xlarge',
                                               'ml.r7i.large',
                                               'ml.r7i.xlarge',
                                               'ml.trn1n.32xlarge'}}}

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.

Note

Use this API if you want to use SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Note

When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

See also: AWS API Documentation

Request Syntax

client.create_endpoint_config(
    EndpointConfigName='string',
    ProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    DataCaptureConfig={
        'EnableCapture': True|False,
        'InitialSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string',
        'CaptureOptions': [
            {
                'CaptureMode': 'Input'|'Output'|'InputAndOutput'
            },
        ],
        'CaptureContentTypeHeader': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    KmsKeyId='string',
    AsyncInferenceConfig={
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    ExplainerConfig={
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    ShadowProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    ExecutionRoleArn='string',
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    EnableNetworkIsolation=True|False
)
type EndpointConfigName

string

param EndpointConfigName

[REQUIRED]

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

type ProductionVariants

list

param ProductionVariants

[REQUIRED]

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

  • (dict) --

    Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

    • VariantName (string) -- [REQUIRED]

      The name of the production variant.

    • ModelName (string) --

      The name of the model that you want to host. This is the name that you specified when creating the model.

    • InitialInstanceCount (integer) --

      Number of instances to launch initially.

    • InstanceType (string) --

      The ML compute instance type.

    • InitialVariantWeight (float) --

      Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

    • AcceleratorType (string) --

      The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

    • CoreDumpConfig (dict) --

      Specifies configuration for a core dump from the model container when the process crashes.

      • DestinationS3Uri (string) -- [REQUIRED]

        The Amazon S3 bucket to send the core dump to.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

        • // KMS Key Alias "alias/ExampleAlias"

        • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

        If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

        The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

    • ServerlessConfig (dict) --

      The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

      • MemorySizeInMB (integer) -- [REQUIRED]

        The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

      • MaxConcurrency (integer) -- [REQUIRED]

        The maximum number of concurrent invocations your serverless endpoint can process.

      • ProvisionedConcurrency (integer) --

        The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

        Note

        This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

    • EnableSSMAccess (boolean) --

      You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

    • ManagedInstanceScaling (dict) --

      Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

      • Status (string) --

        Indicates whether managed instance scaling is enabled.

      • MinInstanceCount (integer) --

        The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

      • MaxInstanceCount (integer) --

        The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

    • RoutingConfig (dict) --

      Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

      • RoutingStrategy (string) -- [REQUIRED]

        Sets how the endpoint routes incoming traffic:

        • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

        • RANDOM : The endpoint routes each request to a randomly chosen instance.

type DataCaptureConfig

dict

param DataCaptureConfig

Configuration to control how SageMaker captures inference data.

  • EnableCapture (boolean) --

    Whether data capture should be enabled or disabled (defaults to enabled).

  • InitialSamplingPercentage (integer) -- [REQUIRED]

    The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

  • DestinationS3Uri (string) -- [REQUIRED]

    The Amazon S3 location used to capture the data.

  • KmsKeyId (string) --

    The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.

    The KmsKeyId can be any of the following formats:

    • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

    • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

    • Alias name: alias/ExampleAlias

    • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

  • CaptureOptions (list) -- [REQUIRED]

    Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

    • (dict) --

      Specifies data Model Monitor will capture.

      • CaptureMode (string) -- [REQUIRED]

        Specify the boundary of data to capture.

  • CaptureContentTypeHeader (dict) --

    Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

    • CsvContentTypes (list) --

      The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.

      • (string) --

    • JsonContentTypes (list) --

      The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

      • (string) --

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type KmsKeyId

string

param KmsKeyId

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

The KmsKeyId can be any of the following formats:

  • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

  • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

  • Alias name: alias/ExampleAlias

  • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section`Using Key Policies in Amazon Web Services KMS <https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html>`__

Note

Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

For a list of instance types that support local instance storage, see Instance Store Volumes .

For more information about local instance storage encryption, see SSD Instance Store Volumes .

type AsyncInferenceConfig

dict

param AsyncInferenceConfig

Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync .

  • ClientConfig (dict) --

    Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

    • MaxConcurrentInvocationsPerInstance (integer) --

      The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

  • OutputConfig (dict) -- [REQUIRED]

    Specifies the configuration for asynchronous inference invocation outputs.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

    • S3OutputPath (string) --

      The Amazon S3 location to upload inference responses to.

    • NotificationConfig (dict) --

      Specifies the configuration for notifications of inference results for asynchronous inference.

      • SuccessTopic (string) --

        Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

      • ErrorTopic (string) --

        Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

      • IncludeInferenceResponseIn (list) --

        The Amazon SNS topics where you want the inference response to be included.

        Note

        The inference response is included only if the response size is less than or equal to 128 KB.

        • (string) --

    • S3FailurePath (string) --

      The Amazon S3 location to upload failure inference responses to.

type ExplainerConfig

dict

param ExplainerConfig

A member of CreateEndpointConfig that enables explainers.

  • ClarifyExplainerConfig (dict) --

    A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

    • EnableExplanations (string) --

      A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

    • InferenceConfig (dict) --

      The inference configuration parameter for the model container.

      • FeaturesAttribute (string) --

        Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

      • ContentTemplate (string) --

        A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

      • MaxRecordCount (integer) --

        The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

      • MaxPayloadInMB (integer) --

        The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

      • ProbabilityIndex (integer) --

        A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

        Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

        Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

      • LabelIndex (integer) --

        A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

        Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

      • ProbabilityAttribute (string) --

        A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

        Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

      • LabelAttribute (string) --

        A JMESPath expression used to locate the list of label headers in the model container output.

        Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

      • LabelHeaders (list) --

        For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

        • (string) --

      • FeatureHeaders (list) --

        The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

        • (string) --

      • FeatureTypes (list) --

        A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

        • (string) --

    • ShapConfig (dict) -- [REQUIRED]

      The configuration for SHAP analysis.

      • ShapBaselineConfig (dict) -- [REQUIRED]

        The configuration for the SHAP baseline of the Kernal SHAP algorithm.

        • MimeType (string) --

          The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

        • ShapBaseline (string) --

          The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

        • ShapBaselineUri (string) --

          The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

      • NumberOfSamples (integer) --

        The number of samples to be used for analysis by the Kernal SHAP algorithm.

        Note

        The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

      • UseLogit (boolean) --

        A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

      • Seed (integer) --

        The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

      • TextConfig (dict) --

        A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

        • Language (string) -- [REQUIRED]

          Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

          Note

          For a mix of multiple languages, use code 'xx' .

        • Granularity (string) -- [REQUIRED]

          The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

type ShadowProductionVariants

list

param ShadowProductionVariants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants . If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants .

  • (dict) --

    Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

    • VariantName (string) -- [REQUIRED]

      The name of the production variant.

    • ModelName (string) --

      The name of the model that you want to host. This is the name that you specified when creating the model.

    • InitialInstanceCount (integer) --

      Number of instances to launch initially.

    • InstanceType (string) --

      The ML compute instance type.

    • InitialVariantWeight (float) --

      Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

    • AcceleratorType (string) --

      The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

    • CoreDumpConfig (dict) --

      Specifies configuration for a core dump from the model container when the process crashes.

      • DestinationS3Uri (string) -- [REQUIRED]

        The Amazon S3 bucket to send the core dump to.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

        • // KMS Key Alias "alias/ExampleAlias"

        • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

        If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

        The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

    • ServerlessConfig (dict) --

      The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

      • MemorySizeInMB (integer) -- [REQUIRED]

        The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

      • MaxConcurrency (integer) -- [REQUIRED]

        The maximum number of concurrent invocations your serverless endpoint can process.

      • ProvisionedConcurrency (integer) --

        The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

        Note

        This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

    • ModelDataDownloadTimeoutInSeconds (integer) --

      The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

    • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

      The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

    • EnableSSMAccess (boolean) --

      You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

    • ManagedInstanceScaling (dict) --

      Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

      • Status (string) --

        Indicates whether managed instance scaling is enabled.

      • MinInstanceCount (integer) --

        The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

      • MaxInstanceCount (integer) --

        The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

    • RoutingConfig (dict) --

      Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

      • RoutingStrategy (string) -- [REQUIRED]

        Sets how the endpoint routes incoming traffic:

        • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

        • RANDOM : The endpoint routes each request to a randomly chosen instance.

type ExecutionRoleArn

string

param ExecutionRoleArn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles .

Note

To be able to pass this role to Amazon SageMaker, the caller of this action must have the iam:PassRole permission.

type VpcConfig

dict

param VpcConfig

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

  • SecurityGroupIds (list) -- [REQUIRED]

    The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

    • (string) --

  • Subnets (list) -- [REQUIRED]

    The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

    • (string) --

type EnableNetworkIsolation

boolean

param EnableNetworkIsolation

Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

rtype

dict

returns

Response Syntax

{
    'EndpointConfigArn': 'string'
}

Response Structure

  • (dict) --

    • EndpointConfigArn (string) --

      The Amazon Resource Name (ARN) of the endpoint configuration.

CreateFeatureGroup (updated) Link ¶
Changes (request)
{'OfflineStoreConfig': {'TableFormat': {'Default'}}}

Create a new FeatureGroup . A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record .

The FeatureGroup defines the schema and features contained in the FeatureGroup . A FeatureGroup definition is composed of a list of Features , a RecordIdentifierFeatureName , an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore . Check Amazon Web Services service quotas to see the FeatureGroup s quota for your Amazon Web Services account.

Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType .

Warning

You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup .

See also: AWS API Documentation

Request Syntax

client.create_feature_group(
    FeatureGroupName='string',
    RecordIdentifierFeatureName='string',
    EventTimeFeatureName='string',
    FeatureDefinitions=[
        {
            'FeatureName': 'string',
            'FeatureType': 'Integral'|'Fractional'|'String',
            'CollectionType': 'List'|'Set'|'Vector',
            'CollectionConfig': {
                'VectorConfig': {
                    'Dimension': 123
                }
            }
        },
    ],
    OnlineStoreConfig={
        'SecurityConfig': {
            'KmsKeyId': 'string'
        },
        'EnableOnlineStore': True|False,
        'TtlDuration': {
            'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
            'Value': 123
        },
        'StorageType': 'Standard'|'InMemory'
    },
    OfflineStoreConfig={
        'S3StorageConfig': {
            'S3Uri': 'string',
            'KmsKeyId': 'string',
            'ResolvedOutputS3Uri': 'string'
        },
        'DisableGlueTableCreation': True|False,
        'DataCatalogConfig': {
            'TableName': 'string',
            'Catalog': 'string',
            'Database': 'string'
        },
        'TableFormat': 'Default'|'Glue'|'Iceberg'
    },
    RoleArn='string',
    Description='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type FeatureGroupName

string

param FeatureGroupName

[REQUIRED]

The name of the FeatureGroup . The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:

  • Must start and end with an alphanumeric character.

  • Can only contain alphanumeric character and hyphens. Spaces are not allowed.

type RecordIdentifierFeatureName

string

param RecordIdentifierFeatureName

[REQUIRED]

The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore . Only the latest record per identifier value will be stored in the OnlineStore . RecordIdentifierFeatureName must be one of feature definitions' names.

You use the RecordIdentifierFeatureName to access data in a FeatureStore .

This name:

  • Must start and end with an alphanumeric character.

  • Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.

type EventTimeFeatureName

string

param EventTimeFeatureName

[REQUIRED]

The name of the feature that stores the EventTime of a Record in a FeatureGroup .

An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .

An EventTime can be a String or Fractional .

  • Fractional : EventTime feature values must be a Unix timestamp in seconds.

  • String : EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy , MM , and dd represent the year, month, and day respectively and HH , mm , ss , and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.

type FeatureDefinitions

list

param FeatureDefinitions

[REQUIRED]

A list of Feature names and types. Name and Type is compulsory per Feature .

Valid feature FeatureType s are Integral , Fractional and String .

FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time

You can create up to 2,500 FeatureDefinition s per FeatureGroup .

  • (dict) --

    A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .

    • FeatureName (string) -- [REQUIRED]

      The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .

    • FeatureType (string) -- [REQUIRED]

      The value type of a feature. Valid values are Integral, Fractional, or String.

    • CollectionType (string) --

      A grouping of elements where each element within the collection must have the same feature type (String , Integral , or Fractional ).

      • List : An ordered collection of elements.

      • Set : An unordered collection of unique elements.

      • Vector : A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.

    • CollectionConfig (dict) --

      Configuration for your collection.

      • VectorConfig (dict) --

        Configuration for your vector collection type.

        • Dimension : The number of elements in your vector.

        • Dimension (integer) -- [REQUIRED]

          The number of elements in your vector.

type OnlineStoreConfig

dict

param OnlineStoreConfig

You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig .

You can also include an Amazon Web Services KMS key ID (KMSKeyId ) for at-rest encryption of the OnlineStore .

The default value is False .

  • SecurityConfig (dict) --

    Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.

      The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :

      • "kms:Encrypt"

      • "kms:Decrypt"

      • "kms:DescribeKey"

      • "kms:CreateGrant"

      • "kms:RetireGrant"

      • "kms:ReEncryptFrom"

      • "kms:ReEncryptTo"

      • "kms:GenerateDataKey"

      • "kms:ListAliases"

      • "kms:ListGrants"

      • "kms:RevokeGrant"

      The caller (either user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :

      • "kms:Decrypt"

  • EnableOnlineStore (boolean) --

    Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.

    The default value is False .

  • TtlDuration (dict) --

    Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration . For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.

    • Unit (string) --

      TtlDuration time unit.

    • Value (integer) --

      TtlDuration time value.

  • StorageType (string) --

    Option for different tiers of low latency storage for real-time data retrieval.

    • Standard : A managed low latency data store for feature groups.

    • InMemory : A managed data store for feature groups that supports very low latency retrieval.

type OfflineStoreConfig

dict

param OfflineStoreConfig

Use this to configure an OfflineFeatureStore . This parameter allows you to specify:

  • The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore .

  • A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.

  • An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore . If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.

  • Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .

To learn more about this parameter, see OfflineStoreConfig .

  • S3StorageConfig (dict) -- [REQUIRED]

    The Amazon Simple Storage (Amazon S3) location of OfflineStore .

    • S3Uri (string) -- [REQUIRED]

      The S3 URI, or location in Amazon S3, of OfflineStore .

      S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.

      The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :

      • "kms:GenerateDataKey"

    • ResolvedOutputS3Uri (string) --

      The S3 path where offline records are written.

  • DisableGlueTableCreation (boolean) --

    Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore . If set to False , Feature Store will name the OfflineStore Glue table following Athena's naming recommendations .

    The default value is False .

  • DataCatalogConfig (dict) --

    The meta data of the Glue table that is autogenerated when an OfflineStore is created.

    • TableName (string) -- [REQUIRED]

      The name of the Glue table.

    • Catalog (string) -- [REQUIRED]

      The name of the Glue table catalog.

    • Database (string) -- [REQUIRED]

      The name of the Glue table database.

  • TableFormat (string) --

    Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .

type RoleArn

string

param RoleArn

The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.

type Description

string

param Description

A free-form description of a FeatureGroup .

type Tags

list

param Tags

Tags used to identify Features in each FeatureGroup .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'FeatureGroupArn': 'string'
}

Response Structure

  • (dict) --

    • FeatureGroupArn (string) --

      The Amazon Resource Name (ARN) of the FeatureGroup . This is a unique identifier for the feature group.

CreateFlowDefinition (updated) Link ¶
Changes (request)
{'HumanLoopRequestSource': {'AwsManagedHumanLoopRequestSource': {'AWS/Bedrock/ModelEvaluation',
                                                                 'AWS/Handshake/VerifyIdentity',
                                                                 'AWS/Textract/AnalyzeExpense'}}}

Creates a flow definition.

See also: AWS API Documentation

Request Syntax

client.create_flow_definition(
    FlowDefinitionName='string',
    HumanLoopRequestSource={
        'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'|'AWS/Textract/AnalyzeExpense'|'AWS/Handshake/VerifyIdentity'|'AWS/Bedrock/ModelEvaluation'
    },
    HumanLoopActivationConfig={
        'HumanLoopActivationConditionsConfig': {
            'HumanLoopActivationConditions': 'string'
        }
    },
    HumanLoopConfig={
        'WorkteamArn': 'string',
        'HumanTaskUiArn': 'string',
        'TaskTitle': 'string',
        'TaskDescription': 'string',
        'TaskCount': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskKeywords': [
            'string',
        ],
        'PublicWorkforceTaskPrice': {
            'AmountInUsd': {
                'Dollars': 123,
                'Cents': 123,
                'TenthFractionsOfACent': 123
            }
        }
    },
    OutputConfig={
        'S3OutputPath': 'string',
        'KmsKeyId': 'string'
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type FlowDefinitionName

string

param FlowDefinitionName

[REQUIRED]

The name of your flow definition.

type HumanLoopRequestSource

dict

param HumanLoopRequestSource

Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

  • AwsManagedHumanLoopRequestSource (string) -- [REQUIRED]

    Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:

type HumanLoopActivationConfig

dict

param HumanLoopActivationConfig

An object containing information about the events that trigger a human workflow.

  • HumanLoopActivationConditionsConfig (dict) -- [REQUIRED]

    Container structure for defining under what conditions SageMaker creates a human loop.

    • HumanLoopActivationConditions (string) -- [REQUIRED]

      JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .

type HumanLoopConfig

dict

param HumanLoopConfig

An object containing information about the tasks the human reviewers will perform.

  • WorkteamArn (string) -- [REQUIRED]

    Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces .

  • HumanTaskUiArn (string) -- [REQUIRED]

    The Amazon Resource Name (ARN) of the human task user interface.

    You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.

    To learn how to create a custom HTML template, see Create Custom Worker Task Template .

    To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates .

  • TaskTitle (string) -- [REQUIRED]

    A title for the human worker task.

  • TaskDescription (string) -- [REQUIRED]

    A description for the human worker task.

  • TaskCount (integer) -- [REQUIRED]

    The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.

  • TaskAvailabilityLifetimeInSeconds (integer) --

    The length of time that a task remains available for review by human workers.

  • TaskTimeLimitInSeconds (integer) --

    The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).

  • TaskKeywords (list) --

    Keywords used to describe the task so that workers can discover the task.

    • (string) --

  • PublicWorkforceTaskPrice (dict) --

    Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

    Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.

    • 0.036

    • 0.048

    • 0.060

    • 0.072

    • 0.120

    • 0.240

    • 0.360

    • 0.480

    • 0.600

    • 0.720

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

    • 0.012

    • 0.024

    • 0.036

    • 0.048

    • 0.060

    • 0.072

    • 0.120

    • 0.240

    • 0.360

    • 0.480

    • 0.600

    • 0.720

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.

    • 2.400

    • 2.280

    • 2.160

    • 2.040

    • 1.920

    • 1.800

    • 1.680

    • 1.560

    • 1.440

    • 1.320

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    • AmountInUsd (dict) --

      Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

      • Dollars (integer) --

        The whole number of dollars in the amount.

      • Cents (integer) --

        The fractional portion, in cents, of the amount.

      • TenthFractionsOfACent (integer) --

        Fractions of a cent, in tenths.

type OutputConfig

dict

param OutputConfig

[REQUIRED]

An object containing information about where the human review results will be uploaded.

  • S3OutputPath (string) -- [REQUIRED]

    The Amazon S3 path where the object containing human output will be made available.

    To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data .

  • KmsKeyId (string) --

    The Amazon Key Management Service (KMS) key ID for server-side encryption.

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298 .

type Tags

list

param Tags

An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'FlowDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • FlowDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the flow definition you create.

CreateHyperParameterTuningJob (updated) Link ¶
Changes (request)
{'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                                       'ml.c6i.16xlarge',
                                                                                                       'ml.c6i.24xlarge',
                                                                                                       'ml.c6i.2xlarge',
                                                                                                       'ml.c6i.32xlarge',
                                                                                                       'ml.c6i.4xlarge',
                                                                                                       'ml.c6i.8xlarge',
                                                                                                       'ml.c6i.xlarge',
                                                                                                       'ml.m6i.12xlarge',
                                                                                                       'ml.m6i.16xlarge',
                                                                                                       'ml.m6i.24xlarge',
                                                                                                       'ml.m6i.2xlarge',
                                                                                                       'ml.m6i.32xlarge',
                                                                                                       'ml.m6i.4xlarge',
                                                                                                       'ml.m6i.8xlarge',
                                                                                                       'ml.m6i.large',
                                                                                                       'ml.m6i.xlarge',
                                                                                                       'ml.p4de.24xlarge'}},
                                                                  'InstanceType': {'ml.c6i.12xlarge',
                                                                                   'ml.c6i.16xlarge',
                                                                                   'ml.c6i.24xlarge',
                                                                                   'ml.c6i.2xlarge',
                                                                                   'ml.c6i.32xlarge',
                                                                                   'ml.c6i.4xlarge',
                                                                                   'ml.c6i.8xlarge',
                                                                                   'ml.c6i.xlarge',
                                                                                   'ml.m6i.12xlarge',
                                                                                   'ml.m6i.16xlarge',
                                                                                   'ml.m6i.24xlarge',
                                                                                   'ml.m6i.2xlarge',
                                                                                   'ml.m6i.32xlarge',
                                                                                   'ml.m6i.4xlarge',
                                                                                   'ml.m6i.8xlarge',
                                                                                   'ml.m6i.large',
                                                                                   'ml.m6i.xlarge',
                                                                                   'ml.p4de.24xlarge'}},
                           'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                  'ml.c6i.16xlarge',
                                                                                  'ml.c6i.24xlarge',
                                                                                  'ml.c6i.2xlarge',
                                                                                  'ml.c6i.32xlarge',
                                                                                  'ml.c6i.4xlarge',
                                                                                  'ml.c6i.8xlarge',
                                                                                  'ml.c6i.xlarge',
                                                                                  'ml.m6i.12xlarge',
                                                                                  'ml.m6i.16xlarge',
                                                                                  'ml.m6i.24xlarge',
                                                                                  'ml.m6i.2xlarge',
                                                                                  'ml.m6i.32xlarge',
                                                                                  'ml.m6i.4xlarge',
                                                                                  'ml.m6i.8xlarge',
                                                                                  'ml.m6i.large',
                                                                                  'ml.m6i.xlarge',
                                                                                  'ml.p4de.24xlarge'}},
                                              'InstanceType': {'ml.c6i.12xlarge',
                                                               'ml.c6i.16xlarge',
                                                               'ml.c6i.24xlarge',
                                                               'ml.c6i.2xlarge',
                                                               'ml.c6i.32xlarge',
                                                               'ml.c6i.4xlarge',
                                                               'ml.c6i.8xlarge',
                                                               'ml.c6i.xlarge',
                                                               'ml.m6i.12xlarge',
                                                               'ml.m6i.16xlarge',
                                                               'ml.m6i.24xlarge',
                                                               'ml.m6i.2xlarge',
                                                               'ml.m6i.32xlarge',
                                                               'ml.m6i.4xlarge',
                                                               'ml.m6i.8xlarge',
                                                               'ml.m6i.large',
                                                               'ml.m6i.xlarge',
                                                               'ml.p4de.24xlarge'}}},
 'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                                        'ml.c6i.16xlarge',
                                                                                                        'ml.c6i.24xlarge',
                                                                                                        'ml.c6i.2xlarge',
                                                                                                        'ml.c6i.32xlarge',
                                                                                                        'ml.c6i.4xlarge',
                                                                                                        'ml.c6i.8xlarge',
                                                                                                        'ml.c6i.xlarge',
                                                                                                        'ml.m6i.12xlarge',
                                                                                                        'ml.m6i.16xlarge',
                                                                                                        'ml.m6i.24xlarge',
                                                                                                        'ml.m6i.2xlarge',
                                                                                                        'ml.m6i.32xlarge',
                                                                                                        'ml.m6i.4xlarge',
                                                                                                        'ml.m6i.8xlarge',
                                                                                                        'ml.m6i.large',
                                                                                                        'ml.m6i.xlarge',
                                                                                                        'ml.p4de.24xlarge'}},
                                                                   'InstanceType': {'ml.c6i.12xlarge',
                                                                                    'ml.c6i.16xlarge',
                                                                                    'ml.c6i.24xlarge',
                                                                                    'ml.c6i.2xlarge',
                                                                                    'ml.c6i.32xlarge',
                                                                                    'ml.c6i.4xlarge',
                                                                                    'ml.c6i.8xlarge',
                                                                                    'ml.c6i.xlarge',
                                                                                    'ml.m6i.12xlarge',
                                                                                    'ml.m6i.16xlarge',
                                                                                    'ml.m6i.24xlarge',
                                                                                    'ml.m6i.2xlarge',
                                                                                    'ml.m6i.32xlarge',
                                                                                    'ml.m6i.4xlarge',
                                                                                    'ml.m6i.8xlarge',
                                                                                    'ml.m6i.large',
                                                                                    'ml.m6i.xlarge',
                                                                                    'ml.p4de.24xlarge'}},
                            'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                   'ml.c6i.16xlarge',
                                                                                   'ml.c6i.24xlarge',
                                                                                   'ml.c6i.2xlarge',
                                                                                   'ml.c6i.32xlarge',
                                                                                   'ml.c6i.4xlarge',
                                                                                   'ml.c6i.8xlarge',
                                                                                   'ml.c6i.xlarge',
                                                                                   'ml.m6i.12xlarge',
                                                                                   'ml.m6i.16xlarge',
                                                                                   'ml.m6i.24xlarge',
                                                                                   'ml.m6i.2xlarge',
                                                                                   'ml.m6i.32xlarge',
                                                                                   'ml.m6i.4xlarge',
                                                                                   'ml.m6i.8xlarge',
                                                                                   'ml.m6i.large',
                                                                                   'ml.m6i.xlarge',
                                                                                   'ml.p4de.24xlarge'}},
                                               'InstanceType': {'ml.c6i.12xlarge',
                                                                'ml.c6i.16xlarge',
                                                                'ml.c6i.24xlarge',
                                                                'ml.c6i.2xlarge',
                                                                'ml.c6i.32xlarge',
                                                                'ml.c6i.4xlarge',
                                                                'ml.c6i.8xlarge',
                                                                'ml.c6i.xlarge',
                                                                'ml.m6i.12xlarge',
                                                                'ml.m6i.16xlarge',
                                                                'ml.m6i.24xlarge',
                                                                'ml.m6i.2xlarge',
                                                                'ml.m6i.32xlarge',
                                                                'ml.m6i.4xlarge',
                                                                'ml.m6i.8xlarge',
                                                                'ml.m6i.large',
                                                                'ml.m6i.xlarge',
                                                                'ml.p4de.24xlarge'}}}}

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components .

Warning

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

See also: AWS API Documentation

Request Syntax

client.create_hyper_parameter_tuning_job(
    HyperParameterTuningJobName='string',
    HyperParameterTuningJobConfig={
        'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
        'StrategyConfig': {
            'HyperbandStrategyConfig': {
                'MinResource': 123,
                'MaxResource': 123
            }
        },
        'HyperParameterTuningJobObjective': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'string'
        },
        'ResourceLimits': {
            'MaxNumberOfTrainingJobs': 123,
            'MaxParallelTrainingJobs': 123,
            'MaxRuntimeInSeconds': 123
        },
        'ParameterRanges': {
            'IntegerParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'ContinuousParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'CategoricalParameterRanges': [
                {
                    'Name': 'string',
                    'Values': [
                        'string',
                    ]
                },
            ],
            'AutoParameters': [
                {
                    'Name': 'string',
                    'ValueHint': 'string'
                },
            ]
        },
        'TrainingJobEarlyStoppingType': 'Off'|'Auto',
        'TuningJobCompletionCriteria': {
            'TargetObjectiveMetricValue': ...,
            'BestObjectiveNotImproving': {
                'MaxNumberOfTrainingJobsNotImproving': 123
            },
            'ConvergenceDetected': {
                'CompleteOnConvergence': 'Disabled'|'Enabled'
            }
        },
        'RandomSeed': 123
    },
    TrainingJobDefinition={
        'DefinitionName': 'string',
        'TuningObjective': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'string'
        },
        'HyperParameterRanges': {
            'IntegerParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'ContinuousParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'CategoricalParameterRanges': [
                {
                    'Name': 'string',
                    'Values': [
                        'string',
                    ]
                },
            ],
            'AutoParameters': [
                {
                    'Name': 'string',
                    'ValueHint': 'string'
                },
            ]
        },
        'StaticHyperParameters': {
            'string': 'string'
        },
        'AlgorithmSpecification': {
            'TrainingImage': 'string',
            'TrainingInputMode': 'Pipe'|'File'|'FastFile',
            'AlgorithmName': 'string',
            'MetricDefinitions': [
                {
                    'Name': 'string',
                    'Regex': 'string'
                },
            ]
        },
        'RoleArn': 'string',
        'InputDataConfig': [
            {
                'ChannelName': 'string',
                'DataSource': {
                    'S3DataSource': {
                        'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                        'S3Uri': 'string',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'AttributeNames': [
                            'string',
                        ],
                        'InstanceGroupNames': [
                            'string',
                        ]
                    },
                    'FileSystemDataSource': {
                        'FileSystemId': 'string',
                        'FileSystemAccessMode': 'rw'|'ro',
                        'FileSystemType': 'EFS'|'FSxLustre',
                        'DirectoryPath': 'string'
                    }
                },
                'ContentType': 'string',
                'CompressionType': 'None'|'Gzip',
                'RecordWrapperType': 'None'|'RecordIO',
                'InputMode': 'Pipe'|'File'|'FastFile',
                'ShuffleConfig': {
                    'Seed': 123
                }
            },
        ],
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        },
        'OutputDataConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'CompressionType': 'GZIP'|'NONE'
        },
        'ResourceConfig': {
            'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
            'InstanceCount': 123,
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string',
            'KeepAlivePeriodInSeconds': 123,
            'InstanceGroups': [
                {
                    'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                    'InstanceCount': 123,
                    'InstanceGroupName': 'string'
                },
            ]
        },
        'HyperParameterTuningResourceConfig': {
            'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
            'InstanceCount': 123,
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string',
            'AllocationStrategy': 'Prioritized',
            'InstanceConfigs': [
                {
                    'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                    'InstanceCount': 123,
                    'VolumeSizeInGB': 123
                },
            ]
        },
        'StoppingCondition': {
            'MaxRuntimeInSeconds': 123,
            'MaxWaitTimeInSeconds': 123,
            'MaxPendingTimeInSeconds': 123
        },
        'EnableNetworkIsolation': True|False,
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableManagedSpotTraining': True|False,
        'CheckpointConfig': {
            'S3Uri': 'string',
            'LocalPath': 'string'
        },
        'RetryStrategy': {
            'MaximumRetryAttempts': 123
        },
        'Environment': {
            'string': 'string'
        }
    },
    TrainingJobDefinitions=[
        {
            'DefinitionName': 'string',
            'TuningObjective': {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'string'
            },
            'HyperParameterRanges': {
                'IntegerParameterRanges': [
                    {
                        'Name': 'string',
                        'MinValue': 'string',
                        'MaxValue': 'string',
                        'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                    },
                ],
                'ContinuousParameterRanges': [
                    {
                        'Name': 'string',
                        'MinValue': 'string',
                        'MaxValue': 'string',
                        'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                    },
                ],
                'CategoricalParameterRanges': [
                    {
                        'Name': 'string',
                        'Values': [
                            'string',
                        ]
                    },
                ],
                'AutoParameters': [
                    {
                        'Name': 'string',
                        'ValueHint': 'string'
                    },
                ]
            },
            'StaticHyperParameters': {
                'string': 'string'
            },
            'AlgorithmSpecification': {
                'TrainingImage': 'string',
                'TrainingInputMode': 'Pipe'|'File'|'FastFile',
                'AlgorithmName': 'string',
                'MetricDefinitions': [
                    {
                        'Name': 'string',
                        'Regex': 'string'
                    },
                ]
            },
            'RoleArn': 'string',
            'InputDataConfig': [
                {
                    'ChannelName': 'string',
                    'DataSource': {
                        'S3DataSource': {
                            'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                            'S3Uri': 'string',
                            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                            'AttributeNames': [
                                'string',
                            ],
                            'InstanceGroupNames': [
                                'string',
                            ]
                        },
                        'FileSystemDataSource': {
                            'FileSystemId': 'string',
                            'FileSystemAccessMode': 'rw'|'ro',
                            'FileSystemType': 'EFS'|'FSxLustre',
                            'DirectoryPath': 'string'
                        }
                    },
                    'ContentType': 'string',
                    'CompressionType': 'None'|'Gzip',
                    'RecordWrapperType': 'None'|'RecordIO',
                    'InputMode': 'Pipe'|'File'|'FastFile',
                    'ShuffleConfig': {
                        'Seed': 123
                    }
                },
            ],
            'VpcConfig': {
                'SecurityGroupIds': [
                    'string',
                ],
                'Subnets': [
                    'string',
                ]
            },
            'OutputDataConfig': {
                'KmsKeyId': 'string',
                'S3OutputPath': 'string',
                'CompressionType': 'GZIP'|'NONE'
            },
            'ResourceConfig': {
                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                'InstanceCount': 123,
                'VolumeSizeInGB': 123,
                'VolumeKmsKeyId': 'string',
                'KeepAlivePeriodInSeconds': 123,
                'InstanceGroups': [
                    {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'InstanceGroupName': 'string'
                    },
                ]
            },
            'HyperParameterTuningResourceConfig': {
                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                'InstanceCount': 123,
                'VolumeSizeInGB': 123,
                'VolumeKmsKeyId': 'string',
                'AllocationStrategy': 'Prioritized',
                'InstanceConfigs': [
                    {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123
                    },
                ]
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 123,
                'MaxWaitTimeInSeconds': 123,
                'MaxPendingTimeInSeconds': 123
            },
            'EnableNetworkIsolation': True|False,
            'EnableInterContainerTrafficEncryption': True|False,
            'EnableManagedSpotTraining': True|False,
            'CheckpointConfig': {
                'S3Uri': 'string',
                'LocalPath': 'string'
            },
            'RetryStrategy': {
                'MaximumRetryAttempts': 123
            },
            'Environment': {
                'string': 'string'
            }
        },
    ],
    WarmStartConfig={
        'ParentHyperParameterTuningJobs': [
            {
                'HyperParameterTuningJobName': 'string'
            },
        ],
        'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    Autotune={
        'Mode': 'Enabled'
    }
)
type HyperParameterTuningJobName

string

param HyperParameterTuningJobName

[REQUIRED]

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

type HyperParameterTuningJobConfig

dict

param HyperParameterTuningJobConfig

[REQUIRED]

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works .

  • Strategy (string) -- [REQUIRED]

    Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .

  • StrategyConfig (dict) --

    The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .

    • HyperbandStrategyConfig (dict) --

      The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.

      • MinResource (integer) --

        The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .

      • MaxResource (integer) --

        The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :

        • epochs

        • numepochs

        • n-epochs

        • n_epochs

        • num_epochs

        If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.

  • HyperParameterTuningJobObjective (dict) --

    The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.

    • Type (string) -- [REQUIRED]

      Whether to minimize or maximize the objective metric.

    • MetricName (string) -- [REQUIRED]

      The name of the metric to use for the objective metric.

  • ResourceLimits (dict) -- [REQUIRED]

    The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.

    • MaxNumberOfTrainingJobs (integer) --

      The maximum number of training jobs that a hyperparameter tuning job can launch.

    • MaxParallelTrainingJobs (integer) -- [REQUIRED]

      The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

    • MaxRuntimeInSeconds (integer) --

      The maximum time in seconds that a hyperparameter tuning job can run.

  • ParameterRanges (dict) --

    The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.

    • IntegerParameterRanges (list) --

      The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

        • Name (string) -- [REQUIRED]

          The name of the hyperparameter to search.

        • MinValue (string) -- [REQUIRED]

          The minimum value of the hyperparameter to search.

        • MaxValue (string) -- [REQUIRED]

          The maximum value of the hyperparameter to search.

        • ScalingType (string) --

          The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

          Auto

          SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

          Linear

          Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

          Logarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

          Logarithmic scaling works only for ranges that have only values greater than 0.

    • ContinuousParameterRanges (list) --

      The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        A list of continuous hyperparameters to tune.

        • Name (string) -- [REQUIRED]

          The name of the continuous hyperparameter to tune.

        • MinValue (string) -- [REQUIRED]

          The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

        • MaxValue (string) -- [REQUIRED]

          The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

        • ScalingType (string) --

          The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

          Auto

          SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

          Linear

          Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

          Logarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

          Logarithmic scaling works only for ranges that have only values greater than 0.

          ReverseLogarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

          Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

    • CategoricalParameterRanges (list) --

      The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        A list of categorical hyperparameters to tune.

        • Name (string) -- [REQUIRED]

          The name of the categorical hyperparameter to tune.

        • Values (list) -- [REQUIRED]

          A list of the categories for the hyperparameter.

          • (string) --

    • AutoParameters (list) --

      A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

      • (dict) --

        The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

        • Name (string) -- [REQUIRED]

          The name of the hyperparameter to optimize using Autotune.

        • ValueHint (string) -- [REQUIRED]

          An example value of the hyperparameter to optimize using Autotune.

  • TrainingJobEarlyStoppingType (string) --

    Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):

    OFF

    Training jobs launched by the hyperparameter tuning job do not use early stopping.

    AUTO

    SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .

  • TuningJobCompletionCriteria (dict) --

    The tuning job's completion criteria.

    • TargetObjectiveMetricValue (float) --

      The value of the objective metric.

    • BestObjectiveNotImproving (dict) --

      A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.

      • MaxNumberOfTrainingJobsNotImproving (integer) --

        The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.

    • ConvergenceDetected (dict) --

      A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.

      • CompleteOnConvergence (string) --

        A flag to stop a tuning job once AMT has detected that the job has converged.

  • RandomSeed (integer) --

    A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

type TrainingJobDefinition

dict

param TrainingJobDefinition

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

  • DefinitionName (string) --

    The job definition name.

  • TuningObjective (dict) --

    Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .

    • Type (string) -- [REQUIRED]

      Whether to minimize or maximize the objective metric.

    • MetricName (string) -- [REQUIRED]

      The name of the metric to use for the objective metric.

  • HyperParameterRanges (dict) --

    Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

    Note

    The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.

    • IntegerParameterRanges (list) --

      The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

        • Name (string) -- [REQUIRED]

          The name of the hyperparameter to search.

        • MinValue (string) -- [REQUIRED]

          The minimum value of the hyperparameter to search.

        • MaxValue (string) -- [REQUIRED]

          The maximum value of the hyperparameter to search.

        • ScalingType (string) --

          The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

          Auto

          SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

          Linear

          Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

          Logarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

          Logarithmic scaling works only for ranges that have only values greater than 0.

    • ContinuousParameterRanges (list) --

      The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        A list of continuous hyperparameters to tune.

        • Name (string) -- [REQUIRED]

          The name of the continuous hyperparameter to tune.

        • MinValue (string) -- [REQUIRED]

          The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

        • MaxValue (string) -- [REQUIRED]

          The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

        • ScalingType (string) --

          The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

          Auto

          SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

          Linear

          Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

          Logarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

          Logarithmic scaling works only for ranges that have only values greater than 0.

          ReverseLogarithmic

          Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

          Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

    • CategoricalParameterRanges (list) --

      The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

      • (dict) --

        A list of categorical hyperparameters to tune.

        • Name (string) -- [REQUIRED]

          The name of the categorical hyperparameter to tune.

        • Values (list) -- [REQUIRED]

          A list of the categories for the hyperparameter.

          • (string) --

    • AutoParameters (list) --

      A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

      • (dict) --

        The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

        • Name (string) -- [REQUIRED]

          The name of the hyperparameter to optimize using Autotune.

        • ValueHint (string) -- [REQUIRED]

          An example value of the hyperparameter to optimize using Autotune.

  • StaticHyperParameters (dict) --

    Specifies the values of hyperparameters that do not change for the tuning job.

    • (string) --

      • (string) --

  • AlgorithmSpecification (dict) -- [REQUIRED]

    The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

    • TrainingImage (string) --

      The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

    • TrainingInputMode (string) -- [REQUIRED]

      The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

      Pipe mode

      If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

      File mode

      If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

      You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

      For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

      FastFile mode

      If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

      FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

    • AlgorithmName (string) --

      The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .

    • MetricDefinitions (list) --

      An array of MetricDefinition objects that specify the metrics that the algorithm emits.

      • (dict) --

        Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

        • Name (string) -- [REQUIRED]

          The name of the metric.

        • Regex (string) -- [REQUIRED]

          A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

  • RoleArn (string) -- [REQUIRED]

    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

  • InputDataConfig (list) --

    An array of Channel objects that specify the input for the training jobs that the tuning job launches.

    • (dict) --

      A channel is a named input source that training algorithms can consume.

      • ChannelName (string) -- [REQUIRED]

        The name of the channel.

      • DataSource (dict) -- [REQUIRED]

        The location of the channel data.

        • S3DataSource (dict) --

          The S3 location of the data source that is associated with a channel.

          • S3DataType (string) -- [REQUIRED]

            If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

            If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

            If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

          • S3Uri (string) -- [REQUIRED]

            Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

            • A key name prefix might look like this: s3://bucketname/exampleprefix

            • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

            Your input bucket must be located in same Amazon Web Services region as your training job.

          • S3DataDistributionType (string) --

            If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

            If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

            Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

            In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

          • AttributeNames (list) --

            A list of one or more attribute names to use that are found in a specified augmented manifest file.

            • (string) --

          • InstanceGroupNames (list) --

            A list of names of instance groups that get data from the S3 data source.

            • (string) --

        • FileSystemDataSource (dict) --

          The file system that is associated with a channel.

          • FileSystemId (string) -- [REQUIRED]

            The file system id.

          • FileSystemAccessMode (string) -- [REQUIRED]

            The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

          • FileSystemType (string) -- [REQUIRED]

            The file system type.

          • DirectoryPath (string) -- [REQUIRED]

            The full path to the directory to associate with the channel.

      • ContentType (string) --

        The MIME type of the data.

      • CompressionType (string) --

        If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

      • RecordWrapperType (string) --

        Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .

        In File mode, leave this field unset or set it to None.

      • InputMode (string) --

        (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

        To use a model for incremental training, choose File input model.

      • ShuffleConfig (dict) --

        A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

        For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

        • Seed (integer) -- [REQUIRED]

          Determines the shuffling order in ShuffleConfig value.

  • VpcConfig (dict) --

    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

      • (string) --

  • OutputDataConfig (dict) -- [REQUIRED]

    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

      • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

      • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

      • // KMS Key Alias "alias/ExampleAlias"

      • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

      If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

      The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

    • S3OutputPath (string) -- [REQUIRED]

      Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

    • CompressionType (string) --

      The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

  • ResourceConfig (dict) --

    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

    Note

    If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

    • InstanceType (string) --

      The ML compute instance type.

      Note

      SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

      Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

      • US East (N. Virginia) (us-east-1)

      • US West (Oregon) (us-west-2)

      To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

    • InstanceCount (integer) --

      The number of ML compute instances to use. For distributed training, provide a value greater than 1.

    • VolumeSizeInGB (integer) -- [REQUIRED]

      The size of the ML storage volume that you want to provision.

      ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

      When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

      When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

      To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .

      To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .

    • VolumeKmsKeyId (string) --

      The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

      Note

      Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

      For a list of instance types that support local instance storage, see Instance Store Volumes .

      For more information about local instance storage encryption, see SSD Instance Store Volumes .

      The VolumeKmsKeyId can be in any of the following formats:

      • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

      • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

    • KeepAlivePeriodInSeconds (integer) --

      The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

    • InstanceGroups (list) --

      The configuration of a heterogeneous cluster in JSON format.

      • (dict) --

        Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

        • InstanceType (string) -- [REQUIRED]

          Specifies the instance type of the instance group.

        • InstanceCount (integer) -- [REQUIRED]

          Specifies the number of instances of the instance group.

        • InstanceGroupName (string) -- [REQUIRED]

          Specifies the name of the instance group.

  • HyperParameterTuningResourceConfig (dict) --

    The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

    • InstanceType (string) --

      The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

    • InstanceCount (integer) --

      The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.

    • VolumeSizeInGB (integer) --

      The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

      Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .

      Note

      SageMaker supports only the General Purpose SSD (gp2) storage volume type.

    • VolumeKmsKeyId (string) --

      A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

      KMS Key ID:

      "1234abcd-12ab-34cd-56ef-1234567890ab"

      Amazon Resource Name (ARN) of a KMS key:

      "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

      Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

    • AllocationStrategy (string) --

      The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

    • InstanceConfigs (list) --

      A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

      Note

      If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .

      • (dict) --

        The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

        • InstanceType (string) -- [REQUIRED]

          The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .

        • InstanceCount (integer) -- [REQUIRED]

          The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

        • VolumeSizeInGB (integer) -- [REQUIRED]

          The volume size in GB of the data to be processed for hyperparameter optimization (optional).

  • StoppingCondition (dict) -- [REQUIRED]

    Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

    • MaxRuntimeInSeconds (integer) --

      The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

      For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

      For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

      The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

    • MaxWaitTimeInSeconds (integer) --

      The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

      When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

    • MaxPendingTimeInSeconds (integer) --

      The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

  • EnableNetworkIsolation (boolean) --

    Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

  • EnableInterContainerTrafficEncryption (boolean) --

    To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

  • EnableManagedSpotTraining (boolean) --

    A Boolean indicating whether managed spot training is enabled (True ) or not (False ).

  • CheckpointConfig (dict) --

    Contains information about the output location for managed spot training checkpoint data.

    • S3Uri (string) -- [REQUIRED]

      Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

    • LocalPath (string) --

      (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

  • RetryStrategy (dict) --

    The number of times to retry the job when the job fails due to an InternalServerError .

    • MaximumRetryAttempts (integer) -- [REQUIRED]

      The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .

  • Environment (dict) --

    An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.

    Note

    The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.

    • (string) --

      • (string) --

type TrainingJobDefinitions

list

param TrainingJobDefinitions

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

  • (dict) --

    Defines the training jobs launched by a hyperparameter tuning job.

    • DefinitionName (string) --

      The job definition name.

    • TuningObjective (dict) --

      Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .

      • Type (string) -- [REQUIRED]

        Whether to minimize or maximize the objective metric.

      • MetricName (string) -- [REQUIRED]

        The name of the metric to use for the objective metric.

    • HyperParameterRanges (dict) --

      Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

      Note

      The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.

      • IntegerParameterRanges (list) --

        The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

          • Name (string) -- [REQUIRED]

            The name of the hyperparameter to search.

          • MinValue (string) -- [REQUIRED]

            The minimum value of the hyperparameter to search.

          • MaxValue (string) -- [REQUIRED]

            The maximum value of the hyperparameter to search.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

      • ContinuousParameterRanges (list) --

        The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of continuous hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the continuous hyperparameter to tune.

          • MinValue (string) -- [REQUIRED]

            The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

          • MaxValue (string) -- [REQUIRED]

            The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

            ReverseLogarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

            Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

      • CategoricalParameterRanges (list) --

        The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of categorical hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the categorical hyperparameter to tune.

          • Values (list) -- [REQUIRED]

            A list of the categories for the hyperparameter.

            • (string) --

      • AutoParameters (list) --

        A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

        • (dict) --

          The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

          • Name (string) -- [REQUIRED]

            The name of the hyperparameter to optimize using Autotune.

          • ValueHint (string) -- [REQUIRED]

            An example value of the hyperparameter to optimize using Autotune.

    • StaticHyperParameters (dict) --

      Specifies the values of hyperparameters that do not change for the tuning job.

      • (string) --

        • (string) --

    • AlgorithmSpecification (dict) -- [REQUIRED]

      The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

      • TrainingImage (string) --

        The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

      • TrainingInputMode (string) -- [REQUIRED]

        The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

        Pipe mode

        If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

        File mode

        If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

        You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

        For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

        FastFile mode

        If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

        FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

      • AlgorithmName (string) --

        The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .

      • MetricDefinitions (list) --

        An array of MetricDefinition objects that specify the metrics that the algorithm emits.

        • (dict) --

          Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

          • Name (string) -- [REQUIRED]

            The name of the metric.

          • Regex (string) -- [REQUIRED]

            A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

    • RoleArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

    • InputDataConfig (list) --

      An array of Channel objects that specify the input for the training jobs that the tuning job launches.

      • (dict) --

        A channel is a named input source that training algorithms can consume.

        • ChannelName (string) -- [REQUIRED]

          The name of the channel.

        • DataSource (dict) -- [REQUIRED]

          The location of the channel data.

          • S3DataSource (dict) --

            The S3 location of the data source that is associated with a channel.

            • S3DataType (string) -- [REQUIRED]

              If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

              If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

              If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

            • S3Uri (string) -- [REQUIRED]

              Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

              • A key name prefix might look like this: s3://bucketname/exampleprefix

              • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

              Your input bucket must be located in same Amazon Web Services region as your training job.

            • S3DataDistributionType (string) --

              If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

              If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

              Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

              In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

            • AttributeNames (list) --

              A list of one or more attribute names to use that are found in a specified augmented manifest file.

              • (string) --

            • InstanceGroupNames (list) --

              A list of names of instance groups that get data from the S3 data source.

              • (string) --

          • FileSystemDataSource (dict) --

            The file system that is associated with a channel.

            • FileSystemId (string) -- [REQUIRED]

              The file system id.

            • FileSystemAccessMode (string) -- [REQUIRED]

              The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

            • FileSystemType (string) -- [REQUIRED]

              The file system type.

            • DirectoryPath (string) -- [REQUIRED]

              The full path to the directory to associate with the channel.

        • ContentType (string) --

          The MIME type of the data.

        • CompressionType (string) --

          If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

        • RecordWrapperType (string) --

          Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .

          In File mode, leave this field unset or set it to None.

        • InputMode (string) --

          (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

          To use a model for incremental training, choose File input model.

        • ShuffleConfig (dict) --

          A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

          For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

          • Seed (integer) -- [REQUIRED]

            Determines the shuffling order in ShuffleConfig value.

    • VpcConfig (dict) --

      The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .

      • SecurityGroupIds (list) -- [REQUIRED]

        The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

        • (string) --

      • Subnets (list) -- [REQUIRED]

        The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

        • (string) --

    • OutputDataConfig (dict) -- [REQUIRED]

      Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

        • // KMS Key Alias "alias/ExampleAlias"

        • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

        If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

        The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

      • S3OutputPath (string) -- [REQUIRED]

        Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

      • CompressionType (string) --

        The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

    • ResourceConfig (dict) --

      The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

      Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

      Note

      If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

      • InstanceType (string) --

        The ML compute instance type.

        Note

        SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

        Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

        • US East (N. Virginia) (us-east-1)

        • US West (Oregon) (us-west-2)

        To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

      • InstanceCount (integer) --

        The number of ML compute instances to use. For distributed training, provide a value greater than 1.

      • VolumeSizeInGB (integer) -- [REQUIRED]

        The size of the ML storage volume that you want to provision.

        ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

        When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

        When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

        To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .

        To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .

      • VolumeKmsKeyId (string) --

        The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

        Note

        Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

        For a list of instance types that support local instance storage, see Instance Store Volumes .

        For more information about local instance storage encryption, see SSD Instance Store Volumes .

        The VolumeKmsKeyId can be in any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

      • KeepAlivePeriodInSeconds (integer) --

        The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

      • InstanceGroups (list) --

        The configuration of a heterogeneous cluster in JSON format.

        • (dict) --

          Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

          • InstanceType (string) -- [REQUIRED]

            Specifies the instance type of the instance group.

          • InstanceCount (integer) -- [REQUIRED]

            Specifies the number of instances of the instance group.

          • InstanceGroupName (string) -- [REQUIRED]

            Specifies the name of the instance group.

    • HyperParameterTuningResourceConfig (dict) --

      The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

      • InstanceType (string) --

        The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

      • InstanceCount (integer) --

        The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.

      • VolumeSizeInGB (integer) --

        The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

        Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .

        Note

        SageMaker supports only the General Purpose SSD (gp2) storage volume type.

      • VolumeKmsKeyId (string) --

        A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

        KMS Key ID:

        "1234abcd-12ab-34cd-56ef-1234567890ab"

        Amazon Resource Name (ARN) of a KMS key:

        "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

        Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

      • AllocationStrategy (string) --

        The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

      • InstanceConfigs (list) --

        A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

        Note

        If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .

        • (dict) --

          The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

          • InstanceType (string) -- [REQUIRED]

            The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .

          • InstanceCount (integer) -- [REQUIRED]

            The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

          • VolumeSizeInGB (integer) -- [REQUIRED]

            The volume size in GB of the data to be processed for hyperparameter optimization (optional).

    • StoppingCondition (dict) -- [REQUIRED]

      Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

      • MaxRuntimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

        For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

        For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

        The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

      • MaxWaitTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

        When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

      • MaxPendingTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

    • EnableNetworkIsolation (boolean) --

      Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

    • EnableInterContainerTrafficEncryption (boolean) --

      To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

    • EnableManagedSpotTraining (boolean) --

      A Boolean indicating whether managed spot training is enabled (True ) or not (False ).

    • CheckpointConfig (dict) --

      Contains information about the output location for managed spot training checkpoint data.

      • S3Uri (string) -- [REQUIRED]

        Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

      • LocalPath (string) --

        (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

    • RetryStrategy (dict) --

      The number of times to retry the job when the job fails due to an InternalServerError .

      • MaximumRetryAttempts (integer) -- [REQUIRED]

        The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .

    • Environment (dict) --

      An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.

      Note

      The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.

      • (string) --

        • (string) --

type WarmStartConfig

dict

param WarmStartConfig

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

Note

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

  • ParentHyperParameterTuningJobs (list) -- [REQUIRED]

    An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .

    Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

    • (dict) --

      A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

      • HyperParameterTuningJobName (string) --

        The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

  • WarmStartType (string) -- [REQUIRED]

    Specifies one of the following:

    IDENTICAL_DATA_AND_ALGORITHM

    The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

    TRANSFER_LEARNING

    The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type Autotune

dict

param Autotune

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

  • ParameterRanges : The names and ranges of parameters that a hyperparameter tuning job can optimize.

  • ResourceLimits : The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

  • TrainingJobEarlyStoppingType : A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

  • RetryStrategy : The number of times to retry a training job.

  • Strategy : Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

  • ConvergenceDetected : A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

  • Mode (string) -- [REQUIRED]

    Set Mode to Enabled if you want to use Autotune.

rtype

dict

returns

Response Syntax

{
    'HyperParameterTuningJobArn': 'string'
}

Response Structure

  • (dict) --

    • HyperParameterTuningJobArn (string) --

      The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.

CreateInferenceRecommendationsJob (updated) Link ¶
Changes (request)
{'InputConfig': {'EndpointConfigurations': {'InstanceType': {'ml.c7i.12xlarge',
                                                             'ml.c7i.16xlarge',
                                                             'ml.c7i.24xlarge',
                                                             'ml.c7i.2xlarge',
                                                             'ml.c7i.48xlarge',
                                                             'ml.c7i.4xlarge',
                                                             'ml.c7i.8xlarge',
                                                             'ml.c7i.large',
                                                             'ml.c7i.xlarge',
                                                             'ml.dl1.24xlarge',
                                                             'ml.m7i.12xlarge',
                                                             'ml.m7i.16xlarge',
                                                             'ml.m7i.24xlarge',
                                                             'ml.m7i.2xlarge',
                                                             'ml.m7i.48xlarge',
                                                             'ml.m7i.4xlarge',
                                                             'ml.m7i.8xlarge',
                                                             'ml.m7i.large',
                                                             'ml.m7i.xlarge',
                                                             'ml.r7i.12xlarge',
                                                             'ml.r7i.16xlarge',
                                                             'ml.r7i.24xlarge',
                                                             'ml.r7i.2xlarge',
                                                             'ml.r7i.48xlarge',
                                                             'ml.r7i.4xlarge',
                                                             'ml.r7i.8xlarge',
                                                             'ml.r7i.large',
                                                             'ml.r7i.xlarge',
                                                             'ml.trn1n.32xlarge'}}}}

Starts a recommendation job. You can create either an instance recommendation or load test job.

See also: AWS API Documentation

Request Syntax

client.create_inference_recommendations_job(
    JobName='string',
    JobType='Default'|'Advanced',
    RoleArn='string',
    InputConfig={
        'ModelPackageVersionArn': 'string',
        'ModelName': 'string',
        'JobDurationInSeconds': 123,
        'TrafficPattern': {
            'TrafficType': 'PHASES'|'STAIRS',
            'Phases': [
                {
                    'InitialNumberOfUsers': 123,
                    'SpawnRate': 123,
                    'DurationInSeconds': 123
                },
            ],
            'Stairs': {
                'DurationInSeconds': 123,
                'NumberOfSteps': 123,
                'UsersPerStep': 123
            }
        },
        'ResourceLimit': {
            'MaxNumberOfTests': 123,
            'MaxParallelOfTests': 123
        },
        'EndpointConfigurations': [
            {
                'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
                'ServerlessConfig': {
                    'MemorySizeInMB': 123,
                    'MaxConcurrency': 123,
                    'ProvisionedConcurrency': 123
                },
                'InferenceSpecificationName': 'string',
                'EnvironmentParameterRanges': {
                    'CategoricalParameterRanges': [
                        {
                            'Name': 'string',
                            'Value': [
                                'string',
                            ]
                        },
                    ]
                }
            },
        ],
        'VolumeKmsKeyId': 'string',
        'ContainerConfig': {
            'Domain': 'string',
            'Task': 'string',
            'Framework': 'string',
            'FrameworkVersion': 'string',
            'PayloadConfig': {
                'SamplePayloadUrl': 'string',
                'SupportedContentTypes': [
                    'string',
                ]
            },
            'NearestModelName': 'string',
            'SupportedInstanceTypes': [
                'string',
            ],
            'SupportedEndpointType': 'RealTime'|'Serverless',
            'DataInputConfig': 'string',
            'SupportedResponseMIMETypes': [
                'string',
            ]
        },
        'Endpoints': [
            {
                'EndpointName': 'string'
            },
        ],
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    JobDescription='string',
    StoppingConditions={
        'MaxInvocations': 123,
        'ModelLatencyThresholds': [
            {
                'Percentile': 'string',
                'ValueInMilliseconds': 123
            },
        ],
        'FlatInvocations': 'Continue'|'Stop'
    },
    OutputConfig={
        'KmsKeyId': 'string',
        'CompiledOutputConfig': {
            'S3OutputUri': 'string'
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type JobName

string

param JobName

[REQUIRED]

A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.

type JobType

string

param JobType

[REQUIRED]

Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT ) job.

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

type InputConfig

dict

param InputConfig

[REQUIRED]

Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.

  • ModelPackageVersionArn (string) --

    The Amazon Resource Name (ARN) of a versioned model package.

  • ModelName (string) --

    The name of the created model.

  • JobDurationInSeconds (integer) --

    Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.

  • TrafficPattern (dict) --

    Specifies the traffic pattern of the job.

    • TrafficType (string) --

      Defines the traffic patterns. Choose either PHASES or STAIRS .

    • Phases (list) --

      Defines the phases traffic specification.

      • (dict) --

        Defines the traffic pattern.

        • InitialNumberOfUsers (integer) --

          Specifies how many concurrent users to start with. The value should be between 1 and 3.

        • SpawnRate (integer) --

          Specified how many new users to spawn in a minute.

        • DurationInSeconds (integer) --

          Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds .

    • Stairs (dict) --

      Defines the stairs traffic pattern.

      • DurationInSeconds (integer) --

        Defines how long each traffic step should be.

      • NumberOfSteps (integer) --

        Specifies how many steps to perform during traffic.

      • UsersPerStep (integer) --

        Specifies how many new users to spawn in each step.

  • ResourceLimit (dict) --

    Defines the resource limit of the job.

    • MaxNumberOfTests (integer) --

      Defines the maximum number of load tests.

    • MaxParallelOfTests (integer) --

      Defines the maximum number of parallel load tests.

  • EndpointConfigurations (list) --

    Specifies the endpoint configuration to use for a job.

    • (dict) --

      The endpoint configuration for the load test.

      • InstanceType (string) --

        The instance types to use for the load test.

      • ServerlessConfig (dict) --

        Specifies the serverless configuration for an endpoint variant.

        • MemorySizeInMB (integer) -- [REQUIRED]

          The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

        • MaxConcurrency (integer) -- [REQUIRED]

          The maximum number of concurrent invocations your serverless endpoint can process.

        • ProvisionedConcurrency (integer) --

          The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

          Note

          This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

      • InferenceSpecificationName (string) --

        The inference specification name in the model package version.

      • EnvironmentParameterRanges (dict) --

        The parameter you want to benchmark against.

        • CategoricalParameterRanges (list) --

          Specified a list of parameters for each category.

          • (dict) --

            Environment parameters you want to benchmark your load test against.

            • Name (string) -- [REQUIRED]

              The Name of the environment variable.

            • Value (list) -- [REQUIRED]

              The list of values you can pass.

              • (string) --

  • VolumeKmsKeyId (string) --

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.

    The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission.

    The KmsKeyId can be any of the following formats:

    • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

    • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"

    • // KMS Key Alias "alias/ExampleAlias"

    • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"

    For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.

  • ContainerConfig (dict) --

    Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.

    • Domain (string) --

      The machine learning domain of the model and its components.

      Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

    • Task (string) --

      The machine learning task that the model accomplishes.

      Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER

    • Framework (string) --

      The machine learning framework of the container image.

      Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN

    • FrameworkVersion (string) --

      The framework version of the container image.

    • PayloadConfig (dict) --

      Specifies the SamplePayloadUrl and all other sample payload-related fields.

      • SamplePayloadUrl (string) --

        The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

      • SupportedContentTypes (list) --

        The supported MIME types for the input data.

        • (string) --

    • NearestModelName (string) --

      The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

      Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet

    • SupportedInstanceTypes (list) --

      A list of the instance types that are used to generate inferences in real-time.

      • (string) --

    • SupportedEndpointType (string) --

      The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.

    • DataInputConfig (string) --

      Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig .

    • SupportedResponseMIMETypes (list) --

      The supported MIME types for the output data.

      • (string) --

  • Endpoints (list) --

    Existing customer endpoints on which to run an Inference Recommender job.

    • (dict) --

      Details about a customer endpoint that was compared in an Inference Recommender job.

      • EndpointName (string) --

        The name of a customer's endpoint.

  • VpcConfig (dict) --

    Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --

    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your model.

      • (string) --

type JobDescription

string

param JobDescription

Description of the recommendation job.

type StoppingConditions

dict

param StoppingConditions

A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.

  • MaxInvocations (integer) --

    The maximum number of requests per minute expected for the endpoint.

  • ModelLatencyThresholds (list) --

    The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.

    • (dict) --

      The model latency threshold.

      • Percentile (string) --

        The model latency percentile threshold. Acceptable values are P95 and P99 . For custom load tests, specify the value as P95 .

      • ValueInMilliseconds (integer) --

        The model latency percentile value in milliseconds.

  • FlatInvocations (string) --

    Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is Stop . If you want the load test to continue after invocations have flattened, set the value to Continue .

type OutputConfig

dict

param OutputConfig

Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.

  • KmsKeyId (string) --

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have kms:GenerateDataKey permission.

    The KmsKeyId can be any of the following formats:

    • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

    • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"

    • // KMS Key Alias "alias/ExampleAlias"

    • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"

    For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.

  • CompiledOutputConfig (dict) --

    Provides information about the output configuration for the compiled model.

    • S3OutputUri (string) --

      Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.

type Tags

list

param Tags

The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'JobArn': 'string'
}

Response Structure

  • (dict) --

    • JobArn (string) --

      The Amazon Resource Name (ARN) of the recommendation job.

CreateModelPackage (updated) Link ¶
Changes (request)
{'AdditionalInferenceSpecifications': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
                                                                                   'ml.c7i.16xlarge',
                                                                                   'ml.c7i.24xlarge',
                                                                                   'ml.c7i.2xlarge',
                                                                                   'ml.c7i.48xlarge',
                                                                                   'ml.c7i.4xlarge',
                                                                                   'ml.c7i.8xlarge',
                                                                                   'ml.c7i.large',
                                                                                   'ml.c7i.xlarge',
                                                                                   'ml.dl1.24xlarge',
                                                                                   'ml.m7i.12xlarge',
                                                                                   'ml.m7i.16xlarge',
                                                                                   'ml.m7i.24xlarge',
                                                                                   'ml.m7i.2xlarge',
                                                                                   'ml.m7i.48xlarge',
                                                                                   'ml.m7i.4xlarge',
                                                                                   'ml.m7i.8xlarge',
                                                                                   'ml.m7i.large',
                                                                                   'ml.m7i.xlarge',
                                                                                   'ml.r7i.12xlarge',
                                                                                   'ml.r7i.16xlarge',
                                                                                   'ml.r7i.24xlarge',
                                                                                   'ml.r7i.2xlarge',
                                                                                   'ml.r7i.48xlarge',
                                                                                   'ml.r7i.4xlarge',
                                                                                   'ml.r7i.8xlarge',
                                                                                   'ml.r7i.large',
                                                                                   'ml.r7i.xlarge',
                                                                                   'ml.trn1n.32xlarge'}},
 'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
                                                                        'ml.c7i.16xlarge',
                                                                        'ml.c7i.24xlarge',
                                                                        'ml.c7i.2xlarge',
                                                                        'ml.c7i.48xlarge',
                                                                        'ml.c7i.4xlarge',
                                                                        'ml.c7i.8xlarge',
                                                                        'ml.c7i.large',
                                                                        'ml.c7i.xlarge',
                                                                        'ml.dl1.24xlarge',
                                                                        'ml.m7i.12xlarge',
                                                                        'ml.m7i.16xlarge',
                                                                        'ml.m7i.24xlarge',
                                                                        'ml.m7i.2xlarge',
                                                                        'ml.m7i.48xlarge',
                                                                        'ml.m7i.4xlarge',
                                                                        'ml.m7i.8xlarge',
                                                                        'ml.m7i.large',
                                                                        'ml.m7i.xlarge',
                                                                        'ml.r7i.12xlarge',
                                                                        'ml.r7i.16xlarge',
                                                                        'ml.r7i.24xlarge',
                                                                        'ml.r7i.2xlarge',
                                                                        'ml.r7i.48xlarge',
                                                                        'ml.r7i.4xlarge',
                                                                        'ml.r7i.8xlarge',
                                                                        'ml.r7i.large',
                                                                        'ml.r7i.xlarge',
                                                                        'ml.trn1n.32xlarge'}}}

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification . To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification .

Note

There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

See also: AWS API Documentation

Request Syntax

client.create_model_package(
    ModelPackageName='string',
    ModelPackageGroupName='string',
    ModelPackageDescription='string',
    InferenceSpecification={
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    ValidationSpecification={
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    SourceAlgorithmSpecification={
        'SourceAlgorithms': [
            {
                'ModelDataUrl': 'string',
                'AlgorithmName': 'string'
            },
        ]
    },
    CertifyForMarketplace=True|False,
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
    MetadataProperties={
        'CommitId': 'string',
        'Repository': 'string',
        'GeneratedBy': 'string',
        'ProjectId': 'string'
    },
    ModelMetrics={
        'ModelQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'ModelDataQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Bias': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PreTrainingReport': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingReport': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        }
    },
    ClientToken='string',
    Domain='string',
    Task='string',
    SamplePayloadUrl='string',
    CustomerMetadataProperties={
        'string': 'string'
    },
    DriftCheckBaselines={
        'Bias': {
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PreTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'ModelQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'ModelDataQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        }
    },
    AdditionalInferenceSpecifications=[
        {
            'Name': 'string',
            'Description': 'string',
            'Containers': [
                {
                    'ContainerHostname': 'string',
                    'Image': 'string',
                    'ImageDigest': 'string',
                    'ModelDataUrl': 'string',
                    'ProductId': 'string',
                    'Environment': {
                        'string': 'string'
                    },
                    'ModelInput': {
                        'DataInputConfig': 'string'
                    },
                    'Framework': 'string',
                    'FrameworkVersion': 'string',
                    'NearestModelName': 'string',
                    'AdditionalS3DataSource': {
                        'S3DataType': 'S3Object',
                        'S3Uri': 'string',
                        'CompressionType': 'None'|'Gzip'
                    }
                },
            ],
            'SupportedTransformInstanceTypes': [
                'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            ],
            'SupportedRealtimeInferenceInstanceTypes': [
                'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            ],
            'SupportedContentTypes': [
                'string',
            ],
            'SupportedResponseMIMETypes': [
                'string',
            ]
        },
    ],
    SkipModelValidation='All'|'None'
)
type ModelPackageName

string

param ModelPackageName

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

type ModelPackageGroupName

string

param ModelPackageGroupName

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

type ModelPackageDescription

string

param ModelPackageDescription

A description of the model package.

type InferenceSpecification

dict

param InferenceSpecification

Specifies details about inference jobs that can be run with models based on this model package, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

  • Containers (list) -- [REQUIRED]

    The Amazon ECR registry path of the Docker image that contains the inference code.

    • (dict) --

      Describes the Docker container for the model package.

      • ContainerHostname (string) --

        The DNS host name for the Docker container.

      • Image (string) -- [REQUIRED]

        The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

        If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

      • ImageDigest (string) --

        An MD5 hash of the training algorithm that identifies the Docker image used for training.

      • ModelDataUrl (string) --

        The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

        Note

        The model artifacts must be in an S3 bucket that is in the same region as the model package.

      • ProductId (string) --

        The Amazon Web Services Marketplace product ID of the model package.

      • Environment (dict) --

        The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

        • (string) --

          • (string) --

      • ModelInput (dict) --

        A structure with Model Input details.

        • DataInputConfig (string) -- [REQUIRED]

          The input configuration object for the model.

      • Framework (string) --

        The machine learning framework of the model package container image.

      • FrameworkVersion (string) --

        The framework version of the Model Package Container Image.

      • NearestModelName (string) --

        The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

      • AdditionalS3DataSource (dict) --

        The additional data source that is used during inference in the Docker container for your model package.

        • S3DataType (string) -- [REQUIRED]

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) -- [REQUIRED]

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

  • SupportedTransformInstanceTypes (list) --

    A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

    This parameter is required for unversioned models, and optional for versioned models.

    • (string) --

  • SupportedRealtimeInferenceInstanceTypes (list) --

    A list of the instance types that are used to generate inferences in real-time.

    This parameter is required for unversioned models, and optional for versioned models.

    • (string) --

  • SupportedContentTypes (list) --

    The supported MIME types for the input data.

    • (string) --

  • SupportedResponseMIMETypes (list) --

    The supported MIME types for the output data.

    • (string) --

type ValidationSpecification

dict

param ValidationSpecification

Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

  • ValidationRole (string) -- [REQUIRED]

    The IAM roles to be used for the validation of the model package.

  • ValidationProfiles (list) -- [REQUIRED]

    An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.

    • (dict) --

      Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

      The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

      • ProfileName (string) -- [REQUIRED]

        The name of the profile for the model package.

      • TransformJobDefinition (dict) -- [REQUIRED]

        The TransformJobDefinition object that describes the transform job used for the validation of the model package.

        • MaxConcurrentTransforms (integer) --

          The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

        • MaxPayloadInMB (integer) --

          The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

        • BatchStrategy (string) --

          A string that determines the number of records included in a single mini-batch.

          SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

        • Environment (dict) --

          The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

          • (string) --

            • (string) --

        • TransformInput (dict) -- [REQUIRED]

          A description of the input source and the way the transform job consumes it.

          • DataSource (dict) -- [REQUIRED]

            Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

            • S3DataSource (dict) -- [REQUIRED]

              The S3 location of the data source that is associated with a channel.

              • S3DataType (string) -- [REQUIRED]

                If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

                If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

                The following values are compatible: ManifestFile , S3Prefix

                The following value is not compatible: AugmentedManifestFile

              • S3Uri (string) -- [REQUIRED]

                Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                • A key name prefix might look like this: s3://bucketname/exampleprefix .

                • A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

          • ContentType (string) --

            The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

          • CompressionType (string) --

            If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .

          • SplitType (string) --

            The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:

            • RecordIO

            • TFRecord

            When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.

            Note

            Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .

            For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.

        • TransformOutput (dict) -- [REQUIRED]

          Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

          • S3OutputPath (string) -- [REQUIRED]

            The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .

            For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

          • Accept (string) --

            The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

          • AssembleWith (string) --

            Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

            If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

            The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • TransformResources (dict) -- [REQUIRED]

          Identifies the ML compute instances for the transform job.

          • InstanceType (string) -- [REQUIRED]

            The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.

          • InstanceCount (integer) -- [REQUIRED]

            The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .

          • VolumeKmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.

            Note

            Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

            For a list of instance types that support local instance storage, see Instance Store Volumes .

            For more information about local instance storage encryption, see SSD Instance Store Volumes .

            The VolumeKmsKeyId can be any of the following formats:

            • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

            • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

            • Alias name: alias/ExampleAlias

            • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

type SourceAlgorithmSpecification

dict

param SourceAlgorithmSpecification

Details about the algorithm that was used to create the model package.

  • SourceAlgorithms (list) -- [REQUIRED]

    A list of the algorithms that were used to create a model package.

    • (dict) --

      Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

      • ModelDataUrl (string) --

        The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

        Note

        The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.

      • AlgorithmName (string) -- [REQUIRED]

        The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

type CertifyForMarketplace

boolean

param CertifyForMarketplace

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

type Tags

list

param Tags

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .

If you supply ModelPackageGroupName , your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type ModelApprovalStatus

string

param ModelApprovalStatus

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

type MetadataProperties

dict

param MetadataProperties

Metadata properties of the tracking entity, trial, or trial component.

  • CommitId (string) --

    The commit ID.

  • Repository (string) --

    The repository.

  • GeneratedBy (string) --

    The entity this entity was generated by.

  • ProjectId (string) --

    The project ID.

type ModelMetrics

dict

param ModelMetrics

A structure that contains model metrics reports.

  • ModelQuality (dict) --

    Metrics that measure the quality of a model.

    • Statistics (dict) --

      Model quality statistics.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      Model quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • ModelDataQuality (dict) --

    Metrics that measure the quality of the input data for a model.

    • Statistics (dict) --

      Data quality statistics for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      Data quality constraints for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Bias (dict) --

    Metrics that measure bais in a model.

    • Report (dict) --

      The bias report for a model

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PreTrainingReport (dict) --

      The pre-training bias report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PostTrainingReport (dict) --

      The post-training bias report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Explainability (dict) --

    Metrics that help explain a model.

    • Report (dict) --

      The explainability report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

type ClientToken

string

param ClientToken

A unique token that guarantees that the call to this API is idempotent.

This field is autopopulated if not provided.

type Domain

string

param Domain

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

type Task

string

param Task

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER" .

Specify "OTHER" if none of the tasks listed fit your use case.

type SamplePayloadUrl

string

param SamplePayloadUrl

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

type CustomerMetadataProperties

dict

param CustomerMetadataProperties

The metadata properties associated with the model package versions.

  • (string) --

    • (string) --

type DriftCheckBaselines

dict

param DriftCheckBaselines

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide .

  • Bias (dict) --

    Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

    • ConfigFile (dict) --

      The bias config file for a model.

      • ContentType (string) --

        The type of content stored in the file source.

      • ContentDigest (string) --

        The digest of the file source.

      • S3Uri (string) -- [REQUIRED]

        The Amazon S3 URI for the file source.

    • PreTrainingConstraints (dict) --

      The pre-training constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PostTrainingConstraints (dict) --

      The post-training constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Explainability (dict) --

    Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

    • Constraints (dict) --

      The drift check explainability constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • ConfigFile (dict) --

      The explainability config file for the model.

      • ContentType (string) --

        The type of content stored in the file source.

      • ContentDigest (string) --

        The digest of the file source.

      • S3Uri (string) -- [REQUIRED]

        The Amazon S3 URI for the file source.

  • ModelQuality (dict) --

    Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

    • Statistics (dict) --

      The drift check model quality statistics.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      The drift check model quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • ModelDataQuality (dict) --

    Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.

    • Statistics (dict) --

      The drift check model data quality statistics.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      The drift check model data quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

type AdditionalInferenceSpecifications

list

param AdditionalInferenceSpecifications

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

  • (dict) --

    A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

    • Name (string) -- [REQUIRED]

      A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.

    • Description (string) --

      A description of the additional Inference specification

    • Containers (list) -- [REQUIRED]

      The Amazon ECR registry path of the Docker image that contains the inference code.

      • (dict) --

        Describes the Docker container for the model package.

        • ContainerHostname (string) --

          The DNS host name for the Docker container.

        • Image (string) -- [REQUIRED]

          The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

          If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

        • ImageDigest (string) --

          An MD5 hash of the training algorithm that identifies the Docker image used for training.

        • ModelDataUrl (string) --

          The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

          Note

          The model artifacts must be in an S3 bucket that is in the same region as the model package.

        • ProductId (string) --

          The Amazon Web Services Marketplace product ID of the model package.

        • Environment (dict) --

          The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

          • (string) --

            • (string) --

        • ModelInput (dict) --

          A structure with Model Input details.

          • DataInputConfig (string) -- [REQUIRED]

            The input configuration object for the model.

        • Framework (string) --

          The machine learning framework of the model package container image.

        • FrameworkVersion (string) --

          The framework version of the Model Package Container Image.

        • NearestModelName (string) --

          The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

        • AdditionalS3DataSource (dict) --

          The additional data source that is used during inference in the Docker container for your model package.

          • S3DataType (string) -- [REQUIRED]

            The data type of the additional data source that you specify for use in inference or training.

          • S3Uri (string) -- [REQUIRED]

            The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

          • CompressionType (string) --

            The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

    • SupportedTransformInstanceTypes (list) --

      A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

      • (string) --

    • SupportedRealtimeInferenceInstanceTypes (list) --

      A list of the instance types that are used to generate inferences in real-time.

      • (string) --

    • SupportedContentTypes (list) --

      The supported MIME types for the input data.

      • (string) --

    • SupportedResponseMIMETypes (list) --

      The supported MIME types for the output data.

      • (string) --

type SkipModelValidation

string

param SkipModelValidation

Indicates if you want to skip model validation.

rtype

dict

returns

Response Syntax

{
    'ModelPackageArn': 'string'
}

Response Structure

  • (dict) --

    • ModelPackageArn (string) --

      The Amazon Resource Name (ARN) of the new model package.

CreateSpace (updated) Link ¶
Changes (request)
{'SpaceSettings': {'AppType': {'Canvas',
                               'DatasetManager',
                               'DetailedProfiler',
                               'Local',
                               'RSession',
                               'SageMakerLite',
                               'Savitur',
                               'VSCode'}}}

Creates a space used for real time collaboration in a Domain.

See also: AWS API Documentation

Request Syntax

client.create_space(
    DomainId='string',
    SpaceName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    SpaceSettings={
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'CodeEditorAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'JupyterLabAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'SageMakerImageVersionAlias': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
        'SpaceStorageSettings': {
            'EbsStorageSettings': {
                'EbsVolumeSizeInGb': 123
            }
        },
        'CustomFileSystems': [
            {
                'EFSFileSystem': {
                    'FileSystemId': 'string'
                }
            },
        ]
    },
    OwnershipSettings={
        'OwnerUserProfileName': 'string'
    },
    SpaceSharingSettings={
        'SharingType': 'Private'|'Shared'
    },
    SpaceDisplayName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The ID of the associated Domain.

type SpaceName

string

param SpaceName

[REQUIRED]

The name of the space.

type Tags

list

param Tags

Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type SpaceSettings

dict

param SpaceSettings

A collection of space settings.

  • JupyterServerAppSettings (dict) --

    The JupyterServer app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • KernelGatewayAppSettings (dict) --

    The KernelGateway app settings.

    • DefaultResourceSpec (dict) --

      The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

      Note

      The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CustomImages (list) --

      A list of custom SageMaker images that are configured to run as a KernelGateway app.

      • (dict) --

        A custom SageMaker image. For more information, see Bring your own SageMaker image .

        • ImageName (string) -- [REQUIRED]

          The name of the CustomImage. Must be unique to your account.

        • ImageVersionNumber (integer) --

          The version number of the CustomImage.

        • AppImageConfigName (string) -- [REQUIRED]

          The name of the AppImageConfig.

    • LifecycleConfigArns (list) --

      The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

      Note

      To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

      • (string) --

  • CodeEditorAppSettings (dict) --

    The Code Editor application settings.

    • DefaultResourceSpec (dict) --

      Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

  • JupyterLabAppSettings (dict) --

    The settings for the JupyterLab application.

    • DefaultResourceSpec (dict) --

      Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • CodeRepositories (list) --

      A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.

      • (dict) --

        A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • RepositoryUrl (string) -- [REQUIRED]

          The URL of the Git repository.

  • AppType (string) --

    The type of app created within the space.

  • SpaceStorageSettings (dict) --

    The storage settings for a private space.

    • EbsStorageSettings (dict) --

      A collection of EBS storage settings for a private space.

      • EbsVolumeSizeInGb (integer) -- [REQUIRED]

        The size of an EBS storage volume for a private space.

  • CustomFileSystems (list) --

    A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.

    • (dict) --

      A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.

      • EFSFileSystem (dict) --

        A custom file system in Amazon EFS.

        • FileSystemId (string) -- [REQUIRED]

          The ID of your Amazon EFS file system.

type OwnershipSettings

dict

param OwnershipSettings

A collection of ownership settings.

  • OwnerUserProfileName (string) -- [REQUIRED]

    The user profile who is the owner of the private space.

type SpaceSharingSettings

dict

param SpaceSharingSettings

A collection of space sharing settings.

  • SharingType (string) -- [REQUIRED]

    Specifies the sharing type of the space.

type SpaceDisplayName

string

param SpaceDisplayName

The name of the space that appears in the SageMaker Studio UI.

rtype

dict

returns

Response Syntax

{
    'SpaceArn': 'string'
}

Response Structure

  • (dict) --

    • SpaceArn (string) --

      The space's Amazon Resource Name (ARN).

CreateStudioLifecycleConfig (updated) Link ¶
Changes (request)
{'StudioLifecycleConfigAppType': {'Savitur', 'VSCode'}}

Creates a new Amazon SageMaker Studio Lifecycle Configuration.

See also: AWS API Documentation

Request Syntax

client.create_studio_lifecycle_config(
    StudioLifecycleConfigName='string',
    StudioLifecycleConfigContent='string',
    StudioLifecycleConfigAppType='JupyterServer'|'KernelGateway'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type StudioLifecycleConfigName

string

param StudioLifecycleConfigName

[REQUIRED]

The name of the Amazon SageMaker Studio Lifecycle Configuration to create.

type StudioLifecycleConfigContent

string

param StudioLifecycleConfigContent

[REQUIRED]

The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.

type StudioLifecycleConfigAppType

string

param StudioLifecycleConfigAppType

[REQUIRED]

The App type that the Lifecycle Configuration is attached to.

type Tags

list

param Tags

Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'StudioLifecycleConfigArn': 'string'
}

Response Structure

  • (dict) --

    • StudioLifecycleConfigArn (string) --

      The ARN of your created Lifecycle Configuration.

CreateTrainingJob (updated) Link ¶
Changes (request)
{'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
                                                        'ml.c6i.16xlarge',
                                                        'ml.c6i.24xlarge',
                                                        'ml.c6i.2xlarge',
                                                        'ml.c6i.32xlarge',
                                                        'ml.c6i.4xlarge',
                                                        'ml.c6i.8xlarge',
                                                        'ml.c6i.xlarge',
                                                        'ml.m6i.12xlarge',
                                                        'ml.m6i.16xlarge',
                                                        'ml.m6i.24xlarge',
                                                        'ml.m6i.2xlarge',
                                                        'ml.m6i.32xlarge',
                                                        'ml.m6i.4xlarge',
                                                        'ml.m6i.8xlarge',
                                                        'ml.m6i.large',
                                                        'ml.m6i.xlarge',
                                                        'ml.p4de.24xlarge'}},
                    'InstanceType': {'ml.c6i.12xlarge',
                                     'ml.c6i.16xlarge',
                                     'ml.c6i.24xlarge',
                                     'ml.c6i.2xlarge',
                                     'ml.c6i.32xlarge',
                                     'ml.c6i.4xlarge',
                                     'ml.c6i.8xlarge',
                                     'ml.c6i.xlarge',
                                     'ml.m6i.12xlarge',
                                     'ml.m6i.16xlarge',
                                     'ml.m6i.24xlarge',
                                     'ml.m6i.2xlarge',
                                     'ml.m6i.32xlarge',
                                     'ml.m6i.4xlarge',
                                     'ml.m6i.8xlarge',
                                     'ml.m6i.large',
                                     'ml.m6i.xlarge',
                                     'ml.p4de.24xlarge'}}}

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.

In the request body, you provide the following:

  • AlgorithmSpecification - Identifies the training algorithm to use.

  • HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .

Warning

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

  • InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.

  • OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.

  • ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

  • EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training .

  • RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.

  • StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.

  • Environment - The environment variables to set in the Docker container.

  • RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError .

For more information about SageMaker, see How It Works .

See also: AWS API Documentation

Request Syntax

client.create_training_job(
    TrainingJobName='string',
    HyperParameters={
        'string': 'string'
    },
    AlgorithmSpecification={
        'TrainingImage': 'string',
        'AlgorithmName': 'string',
        'TrainingInputMode': 'Pipe'|'File'|'FastFile',
        'MetricDefinitions': [
            {
                'Name': 'string',
                'Regex': 'string'
            },
        ],
        'EnableSageMakerMetricsTimeSeries': True|False,
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'TrainingImageConfig': {
            'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
            'TrainingRepositoryAuthConfig': {
                'TrainingRepositoryCredentialsProviderArn': 'string'
            }
        }
    },
    RoleArn='string',
    InputDataConfig=[
        {
            'ChannelName': 'string',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string',
                    'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                    'AttributeNames': [
                        'string',
                    ],
                    'InstanceGroupNames': [
                        'string',
                    ]
                },
                'FileSystemDataSource': {
                    'FileSystemId': 'string',
                    'FileSystemAccessMode': 'rw'|'ro',
                    'FileSystemType': 'EFS'|'FSxLustre',
                    'DirectoryPath': 'string'
                }
            },
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'RecordWrapperType': 'None'|'RecordIO',
            'InputMode': 'Pipe'|'File'|'FastFile',
            'ShuffleConfig': {
                'Seed': 123
            }
        },
    ],
    OutputDataConfig={
        'KmsKeyId': 'string',
        'S3OutputPath': 'string',
        'CompressionType': 'GZIP'|'NONE'
    },
    ResourceConfig={
        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
        'InstanceCount': 123,
        'VolumeSizeInGB': 123,
        'VolumeKmsKeyId': 'string',
        'KeepAlivePeriodInSeconds': 123,
        'InstanceGroups': [
            {
                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                'InstanceCount': 123,
                'InstanceGroupName': 'string'
            },
        ]
    },
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123,
        'MaxPendingTimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    EnableNetworkIsolation=True|False,
    EnableInterContainerTrafficEncryption=True|False,
    EnableManagedSpotTraining=True|False,
    CheckpointConfig={
        'S3Uri': 'string',
        'LocalPath': 'string'
    },
    DebugHookConfig={
        'LocalPath': 'string',
        'S3OutputPath': 'string',
        'HookParameters': {
            'string': 'string'
        },
        'CollectionConfigurations': [
            {
                'CollectionName': 'string',
                'CollectionParameters': {
                    'string': 'string'
                }
            },
        ]
    },
    DebugRuleConfigurations=[
        {
            'RuleConfigurationName': 'string',
            'LocalPath': 'string',
            'S3OutputPath': 'string',
            'RuleEvaluatorImage': 'string',
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'RuleParameters': {
                'string': 'string'
            }
        },
    ],
    TensorBoardOutputConfig={
        'LocalPath': 'string',
        'S3OutputPath': 'string'
    },
    ExperimentConfig={
        'ExperimentName': 'string',
        'TrialName': 'string',
        'TrialComponentDisplayName': 'string',
        'RunName': 'string'
    },
    ProfilerConfig={
        'S3OutputPath': 'string',
        'ProfilingIntervalInMilliseconds': 123,
        'ProfilingParameters': {
            'string': 'string'
        },
        'DisableProfiler': True|False
    },
    ProfilerRuleConfigurations=[
        {
            'RuleConfigurationName': 'string',
            'LocalPath': 'string',
            'S3OutputPath': 'string',
            'RuleEvaluatorImage': 'string',
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'RuleParameters': {
                'string': 'string'
            }
        },
    ],
    Environment={
        'string': 'string'
    },
    RetryStrategy={
        'MaximumRetryAttempts': 123
    },
    InfraCheckConfig={
        'EnableInfraCheck': True|False
    }
)
type TrainingJobName

string

param TrainingJobName

[REQUIRED]

The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

type HyperParameters

dict

param HyperParameters

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint .

Warning

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

  • (string) --

    • (string) --

type AlgorithmSpecification

dict

param AlgorithmSpecification

[REQUIRED]

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .

  • TrainingImage (string) --

    The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .

    Note

    You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

    For more information, see the note in the AlgorithmName parameter description.

  • AlgorithmName (string) --

    The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.

    Note

    You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

    Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.

    If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

  • TrainingInputMode (string) -- [REQUIRED]

    The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

    Pipe mode

    If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

    File mode

    If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

    You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

    For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

    FastFile mode

    If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

    FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

  • MetricDefinitions (list) --

    A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

    • (dict) --

      Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

      • Name (string) -- [REQUIRED]

        The name of the metric.

      • Regex (string) -- [REQUIRED]

        A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

  • EnableSageMakerMetricsTimeSeries (boolean) --

    To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:

  • ContainerEntrypoint (list) --

    The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

    • (string) --

  • ContainerArguments (list) --

    The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

    • (string) --

  • TrainingImageConfig (dict) --

    The configuration to use an image from a private Docker registry for a training job.

    • TrainingRepositoryAccessMode (string) -- [REQUIRED]

      The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .

    • TrainingRepositoryAuthConfig (dict) --

      An object containing authentication information for a private Docker registry containing your training images.

      • TrainingRepositoryCredentialsProviderArn (string) -- [REQUIRED]

        The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.

During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles .

Note

To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

type InputDataConfig

list

param InputDataConfig

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data . The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.

Your input must be in the same Amazon Web Services region as your training job.

  • (dict) --

    A channel is a named input source that training algorithms can consume.

    • ChannelName (string) -- [REQUIRED]

      The name of the channel.

    • DataSource (dict) -- [REQUIRED]

      The location of the channel data.

      • S3DataSource (dict) --

        The S3 location of the data source that is associated with a channel.

        • S3DataType (string) -- [REQUIRED]

          If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

          If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

          If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

        • S3Uri (string) -- [REQUIRED]

          Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

          • A key name prefix might look like this: s3://bucketname/exampleprefix

          • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

          Your input bucket must be located in same Amazon Web Services region as your training job.

        • S3DataDistributionType (string) --

          If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

          If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

          Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

          In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

        • AttributeNames (list) --

          A list of one or more attribute names to use that are found in a specified augmented manifest file.

          • (string) --

        • InstanceGroupNames (list) --

          A list of names of instance groups that get data from the S3 data source.

          • (string) --

      • FileSystemDataSource (dict) --

        The file system that is associated with a channel.

        • FileSystemId (string) -- [REQUIRED]

          The file system id.

        • FileSystemAccessMode (string) -- [REQUIRED]

          The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

        • FileSystemType (string) -- [REQUIRED]

          The file system type.

        • DirectoryPath (string) -- [REQUIRED]

          The full path to the directory to associate with the channel.

    • ContentType (string) --

      The MIME type of the data.

    • CompressionType (string) --

      If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

    • RecordWrapperType (string) --

      Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .

      In File mode, leave this field unset or set it to None.

    • InputMode (string) --

      (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

      To use a model for incremental training, choose File input model.

    • ShuffleConfig (dict) --

      A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

      For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

      • Seed (integer) -- [REQUIRED]

        Determines the shuffling order in ShuffleConfig value.

type OutputDataConfig

dict

param OutputDataConfig

[REQUIRED]

Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

  • KmsKeyId (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

    • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

    • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

    • // KMS Key Alias "alias/ExampleAlias"

    • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

    If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

    The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

  • S3OutputPath (string) -- [REQUIRED]

    Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

  • CompressionType (string) --

    The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

type ResourceConfig

dict

param ResourceConfig

[REQUIRED]

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

  • InstanceType (string) --

    The ML compute instance type.

    Note

    SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

    Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

    • US East (N. Virginia) (us-east-1)

    • US West (Oregon) (us-west-2)

    To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

  • InstanceCount (integer) --

    The number of ML compute instances to use. For distributed training, provide a value greater than 1.

  • VolumeSizeInGB (integer) -- [REQUIRED]

    The size of the ML storage volume that you want to provision.

    ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

    When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

    When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

    To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .

    To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .

  • VolumeKmsKeyId (string) --

    The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

    Note

    Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

    For a list of instance types that support local instance storage, see Instance Store Volumes .

    For more information about local instance storage encryption, see SSD Instance Store Volumes .

    The VolumeKmsKeyId can be in any of the following formats:

    • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

    • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • KeepAlivePeriodInSeconds (integer) --

    The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

  • InstanceGroups (list) --

    The configuration of a heterogeneous cluster in JSON format.

    • (dict) --

      Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

      • InstanceType (string) -- [REQUIRED]

        Specifies the instance type of the instance group.

      • InstanceCount (integer) -- [REQUIRED]

        Specifies the number of instances of the instance group.

      • InstanceGroupName (string) -- [REQUIRED]

        Specifies the name of the instance group.

type VpcConfig

dict

param VpcConfig

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .

  • SecurityGroupIds (list) -- [REQUIRED]

    The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

    • (string) --

  • Subnets (list) -- [REQUIRED]

    The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

    • (string) --

type StoppingCondition

dict

param StoppingCondition

[REQUIRED]

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • MaxRuntimeInSeconds (integer) --

    The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

    For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

    For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

    The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

  • MaxWaitTimeInSeconds (integer) --

    The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

    When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

  • MaxPendingTimeInSeconds (integer) --

    The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

type Tags

list

param Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

type EnableNetworkIsolation

boolean

param EnableNetworkIsolation

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

type EnableInterContainerTrafficEncryption

boolean

param EnableInterContainerTrafficEncryption

To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job .

type EnableManagedSpotTraining

boolean

param EnableManagedSpotTraining

To train models using managed spot training, choose True . Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.

The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

type CheckpointConfig

dict

param CheckpointConfig

Contains information about the output location for managed spot training checkpoint data.

  • S3Uri (string) -- [REQUIRED]

    Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

  • LocalPath (string) --

    (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

type DebugHookConfig

dict

param DebugHookConfig

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .

  • LocalPath (string) --

    Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .

  • S3OutputPath (string) -- [REQUIRED]

    Path to Amazon S3 storage location for metrics and tensors.

  • HookParameters (dict) --

    Configuration information for the Amazon SageMaker Debugger hook parameters.

    • (string) --

      • (string) --

  • CollectionConfigurations (list) --

    Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .

    • (dict) --

      Configuration information for the Amazon SageMaker Debugger output tensor collections.

      • CollectionName (string) --

        The name of the tensor collection. The name must be unique relative to other rule configuration names.

      • CollectionParameters (dict) --

        Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .

        • (string) --

          • (string) --

type DebugRuleConfigurations

list

param DebugRuleConfigurations

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

  • (dict) --

    Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .

    • RuleConfigurationName (string) -- [REQUIRED]

      The name of the rule configuration. It must be unique relative to other rule configuration names.

    • LocalPath (string) --

      Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .

    • S3OutputPath (string) --

      Path to Amazon S3 storage location for rules.

    • RuleEvaluatorImage (string) -- [REQUIRED]

      The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

    • InstanceType (string) --

      The instance type to deploy a custom rule for debugging a training job.

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to the processing instance.

    • RuleParameters (dict) --

      Runtime configuration for rule container.

      • (string) --

        • (string) --

type TensorBoardOutputConfig

dict

param TensorBoardOutputConfig

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

  • LocalPath (string) --

    Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .

  • S3OutputPath (string) -- [REQUIRED]

    Path to Amazon S3 storage location for TensorBoard output.

type ExperimentConfig

dict

param ExperimentConfig

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

  • ExperimentName (string) --

    The name of an existing experiment to associate with the trial component.

  • TrialName (string) --

    The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

  • TrialComponentDisplayName (string) --

    The display name for the trial component. If this key isn't specified, the display name is the trial component name.

  • RunName (string) --

    The name of the experiment run to associate with the trial component.

type ProfilerConfig

dict

param ProfilerConfig

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

  • S3OutputPath (string) --

    Path to Amazon S3 storage location for system and framework metrics.

  • ProfilingIntervalInMilliseconds (integer) --

    A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.

  • ProfilingParameters (dict) --

    Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .

    • (string) --

      • (string) --

  • DisableProfiler (boolean) --

    Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .

type ProfilerRuleConfigurations

list

param ProfilerRuleConfigurations

Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

  • (dict) --

    Configuration information for profiling rules.

    • RuleConfigurationName (string) -- [REQUIRED]

      The name of the rule configuration. It must be unique relative to other rule configuration names.

    • LocalPath (string) --

      Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .

    • S3OutputPath (string) --

      Path to Amazon S3 storage location for rules.

    • RuleEvaluatorImage (string) -- [REQUIRED]

      The Amazon Elastic Container Registry Image for the managed rule evaluation.

    • InstanceType (string) --

      The instance type to deploy a custom rule for profiling a training job.

    • VolumeSizeInGB (integer) --

      The size, in GB, of the ML storage volume attached to the processing instance.

    • RuleParameters (dict) --

      Runtime configuration for rule container.

      • (string) --

        • (string) --

type Environment

dict

param Environment

The environment variables to set in the Docker container.

  • (string) --

    • (string) --

type RetryStrategy

dict

param RetryStrategy

The number of times to retry the job when the job fails due to an InternalServerError .

  • MaximumRetryAttempts (integer) -- [REQUIRED]

    The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .

type InfraCheckConfig

dict

param InfraCheckConfig

Contains information about the infrastructure health check configuration for the training job.

  • EnableInfraCheck (boolean) --

    Enables an infrastructure health check.

rtype

dict

returns

Response Syntax

{
    'TrainingJobArn': 'string'
}

Response Structure

  • (dict) --

    • TrainingJobArn (string) --

      The Amazon Resource Name (ARN) of the training job.

DeleteApp (updated) Link ¶
Changes (request)
{'AppType': {'Canvas',
             'DatasetManager',
             'DetailedProfiler',
             'Local',
             'RSession',
             'SageMakerLite',
             'Savitur',
             'VSCode'}}

Used to stop and delete an app.

See also: AWS API Documentation

Request Syntax

client.delete_app(
    DomainId='string',
    UserProfileName='string',
    SpaceName='string',
    AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
    AppName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

The user profile name. If this value is not set, then SpaceName must be set.

type SpaceName

string

param SpaceName

The name of the space. If this value is not set, then UserProfileName must be set.

type AppType

string

param AppType

[REQUIRED]

The type of app.

type AppName

string

param AppName

[REQUIRED]

The name of the app.

returns

None

DescribeAlgorithm (updated) Link ¶
Changes (response)
{'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
                                                                        'ml.c7i.16xlarge',
                                                                        'ml.c7i.24xlarge',
                                                                        'ml.c7i.2xlarge',
                                                                        'ml.c7i.48xlarge',
                                                                        'ml.c7i.4xlarge',
                                                                        'ml.c7i.8xlarge',
                                                                        'ml.c7i.large',
                                                                        'ml.c7i.xlarge',
                                                                        'ml.dl1.24xlarge',
                                                                        'ml.m7i.12xlarge',
                                                                        'ml.m7i.16xlarge',
                                                                        'ml.m7i.24xlarge',
                                                                        'ml.m7i.2xlarge',
                                                                        'ml.m7i.48xlarge',
                                                                        'ml.m7i.4xlarge',
                                                                        'ml.m7i.8xlarge',
                                                                        'ml.m7i.large',
                                                                        'ml.m7i.xlarge',
                                                                        'ml.r7i.12xlarge',
                                                                        'ml.r7i.16xlarge',
                                                                        'ml.r7i.24xlarge',
                                                                        'ml.r7i.2xlarge',
                                                                        'ml.r7i.48xlarge',
                                                                        'ml.r7i.4xlarge',
                                                                        'ml.r7i.8xlarge',
                                                                        'ml.r7i.large',
                                                                        'ml.r7i.xlarge',
                                                                        'ml.trn1n.32xlarge'}},
 'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.c6i.12xlarge',
                                                              'ml.c6i.16xlarge',
                                                              'ml.c6i.24xlarge',
                                                              'ml.c6i.2xlarge',
                                                              'ml.c6i.32xlarge',
                                                              'ml.c6i.4xlarge',
                                                              'ml.c6i.8xlarge',
                                                              'ml.c6i.xlarge',
                                                              'ml.m6i.12xlarge',
                                                              'ml.m6i.16xlarge',
                                                              'ml.m6i.24xlarge',
                                                              'ml.m6i.2xlarge',
                                                              'ml.m6i.32xlarge',
                                                              'ml.m6i.4xlarge',
                                                              'ml.m6i.8xlarge',
                                                              'ml.m6i.large',
                                                              'ml.m6i.xlarge',
                                                              'ml.p4de.24xlarge'}},
 'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
                                                                                                                                     'ml.c6i.16xlarge',
                                                                                                                                     'ml.c6i.24xlarge',
                                                                                                                                     'ml.c6i.2xlarge',
                                                                                                                                     'ml.c6i.32xlarge',
                                                                                                                                     'ml.c6i.4xlarge',
                                                                                                                                     'ml.c6i.8xlarge',
                                                                                                                                     'ml.c6i.xlarge',
                                                                                                                                     'ml.m6i.12xlarge',
                                                                                                                                     'ml.m6i.16xlarge',
                                                                                                                                     'ml.m6i.24xlarge',
                                                                                                                                     'ml.m6i.2xlarge',
                                                                                                                                     'ml.m6i.32xlarge',
                                                                                                                                     'ml.m6i.4xlarge',
                                                                                                                                     'ml.m6i.8xlarge',
                                                                                                                                     'ml.m6i.large',
                                                                                                                                     'ml.m6i.xlarge',
                                                                                                                                     'ml.p4de.24xlarge'}},
                                                                                                 'InstanceType': {'ml.c6i.12xlarge',
                                                                                                                  'ml.c6i.16xlarge',
                                                                                                                  'ml.c6i.24xlarge',
                                                                                                                  'ml.c6i.2xlarge',
                                                                                                                  'ml.c6i.32xlarge',
                                                                                                                  'ml.c6i.4xlarge',
                                                                                                                  'ml.c6i.8xlarge',
                                                                                                                  'ml.c6i.xlarge',
                                                                                                                  'ml.m6i.12xlarge',
                                                                                                                  'ml.m6i.16xlarge',
                                                                                                                  'ml.m6i.24xlarge',
                                                                                                                  'ml.m6i.2xlarge',
                                                                                                                  'ml.m6i.32xlarge',
                                                                                                                  'ml.m6i.4xlarge',
                                                                                                                  'ml.m6i.8xlarge',
                                                                                                                  'ml.m6i.large',
                                                                                                                  'ml.m6i.xlarge',
                                                                                                                  'ml.p4de.24xlarge'}}}}}}

Returns a description of the specified algorithm that is in your account.

See also: AWS API Documentation

Request Syntax

client.describe_algorithm(
    AlgorithmName='string'
)
type AlgorithmName

string

param AlgorithmName

[REQUIRED]

The name of the algorithm to describe.

rtype

dict

returns

Response Syntax

{
    'AlgorithmName': 'string',
    'AlgorithmArn': 'string',
    'AlgorithmDescription': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'TrainingSpecification': {
        'TrainingImage': 'string',
        'TrainingImageDigest': 'string',
        'SupportedHyperParameters': [
            {
                'Name': 'string',
                'Description': 'string',
                'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
                'Range': {
                    'IntegerParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'ContinuousParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'CategoricalParameterRangeSpecification': {
                        'Values': [
                            'string',
                        ]
                    }
                },
                'IsTunable': True|False,
                'IsRequired': True|False,
                'DefaultValue': 'string'
            },
        ],
        'SupportedTrainingInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
        ],
        'SupportsDistributedTraining': True|False,
        'MetricDefinitions': [
            {
                'Name': 'string',
                'Regex': 'string'
            },
        ],
        'TrainingChannels': [
            {
                'Name': 'string',
                'Description': 'string',
                'IsRequired': True|False,
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedCompressionTypes': [
                    'None'|'Gzip',
                ],
                'SupportedInputModes': [
                    'Pipe'|'File'|'FastFile',
                ]
            },
        ],
        'SupportedTuningJobObjectiveMetrics': [
            {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'string'
            },
        ],
        'AdditionalS3DataSource': {
            'S3DataType': 'S3Object',
            'S3Uri': 'string',
            'CompressionType': 'None'|'Gzip'
        }
    },
    'InferenceSpecification': {
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    'ValidationSpecification': {
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TrainingJobDefinition': {
                    'TrainingInputMode': 'Pipe'|'File'|'FastFile',
                    'HyperParameters': {
                        'string': 'string'
                    },
                    'InputDataConfig': [
                        {
                            'ChannelName': 'string',
                            'DataSource': {
                                'S3DataSource': {
                                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                    'S3Uri': 'string',
                                    'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                                    'AttributeNames': [
                                        'string',
                                    ],
                                    'InstanceGroupNames': [
                                        'string',
                                    ]
                                },
                                'FileSystemDataSource': {
                                    'FileSystemId': 'string',
                                    'FileSystemAccessMode': 'rw'|'ro',
                                    'FileSystemType': 'EFS'|'FSxLustre',
                                    'DirectoryPath': 'string'
                                }
                            },
                            'ContentType': 'string',
                            'CompressionType': 'None'|'Gzip',
                            'RecordWrapperType': 'None'|'RecordIO',
                            'InputMode': 'Pipe'|'File'|'FastFile',
                            'ShuffleConfig': {
                                'Seed': 123
                            }
                        },
                    ],
                    'OutputDataConfig': {
                        'KmsKeyId': 'string',
                        'S3OutputPath': 'string',
                        'CompressionType': 'GZIP'|'NONE'
                    },
                    'ResourceConfig': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123,
                        'VolumeKmsKeyId': 'string',
                        'KeepAlivePeriodInSeconds': 123,
                        'InstanceGroups': [
                            {
                                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                                'InstanceCount': 123,
                                'InstanceGroupName': 'string'
                            },
                        ]
                    },
                    'StoppingCondition': {
                        'MaxRuntimeInSeconds': 123,
                        'MaxWaitTimeInSeconds': 123,
                        'MaxPendingTimeInSeconds': 123
                    }
                },
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
    'AlgorithmStatusDetails': {
        'ValidationStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ],
        'ImageScanStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ]
    },
    'ProductId': 'string',
    'CertifyForMarketplace': True|False
}

Response Structure

  • (dict) --

    • AlgorithmName (string) --

      The name of the algorithm being described.

    • AlgorithmArn (string) --

      The Amazon Resource Name (ARN) of the algorithm.

    • AlgorithmDescription (string) --

      A brief summary about the algorithm.

    • CreationTime (datetime) --

      A timestamp specifying when the algorithm was created.

    • TrainingSpecification (dict) --

      Details about training jobs run by this algorithm.

      • TrainingImage (string) --

        The Amazon ECR registry path of the Docker image that contains the training algorithm.

      • TrainingImageDigest (string) --

        An MD5 hash of the training algorithm that identifies the Docker image used for training.

      • SupportedHyperParameters (list) --

        A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

        • (dict) --

          Defines a hyperparameter to be used by an algorithm.

          • Name (string) --

            The name of this hyperparameter. The name must be unique.

          • Description (string) --

            A brief description of the hyperparameter.

          • Type (string) --

            The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .

          • Range (dict) --

            The allowed range for this hyperparameter.

            • IntegerParameterRangeSpecification (dict) --

              A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

              • MinValue (string) --

                The minimum integer value allowed.

              • MaxValue (string) --

                The maximum integer value allowed.

            • ContinuousParameterRangeSpecification (dict) --

              A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

              • MinValue (string) --

                The minimum floating-point value allowed.

              • MaxValue (string) --

                The maximum floating-point value allowed.

            • CategoricalParameterRangeSpecification (dict) --

              A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

              • Values (list) --

                The allowed categories for the hyperparameter.

                • (string) --

          • IsTunable (boolean) --

            Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

          • IsRequired (boolean) --

            Indicates whether this hyperparameter is required.

          • DefaultValue (string) --

            The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

      • SupportedTrainingInstanceTypes (list) --

        A list of the instance types that this algorithm can use for training.

        • (string) --

      • SupportsDistributedTraining (boolean) --

        Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

      • MetricDefinitions (list) --

        A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

        • (dict) --

          Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

          • Name (string) --

            The name of the metric.

          • Regex (string) --

            A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

      • TrainingChannels (list) --

        A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

        • (dict) --

          Defines a named input source, called a channel, to be used by an algorithm.

          • Name (string) --

            The name of the channel.

          • Description (string) --

            A brief description of the channel.

          • IsRequired (boolean) --

            Indicates whether the channel is required by the algorithm.

          • SupportedContentTypes (list) --

            The supported MIME types for the data.

            • (string) --

          • SupportedCompressionTypes (list) --

            The allowed compression types, if data compression is used.

            • (string) --

          • SupportedInputModes (list) --

            The allowed input mode, either FILE or PIPE.

            In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

            In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

            • (string) --

              The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

              Pipe mode

              If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

              File mode

              If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

              You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

              For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

              FastFile mode

              If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

              FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

      • SupportedTuningJobObjectiveMetrics (list) --

        A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

        • (dict) --

          Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .

          • Type (string) --

            Whether to minimize or maximize the objective metric.

          • MetricName (string) --

            The name of the metric to use for the objective metric.

      • AdditionalS3DataSource (dict) --

        The additional data source used during the training job.

        • S3DataType (string) --

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) --

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

    • InferenceSpecification (dict) --

      Details about inference jobs that the algorithm runs.

      • Containers (list) --

        The Amazon ECR registry path of the Docker image that contains the inference code.

        • (dict) --

          Describes the Docker container for the model package.

          • ContainerHostname (string) --

            The DNS host name for the Docker container.

          • Image (string) --

            The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

            If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

          • ImageDigest (string) --

            An MD5 hash of the training algorithm that identifies the Docker image used for training.

          • ModelDataUrl (string) --

            The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

            Note

            The model artifacts must be in an S3 bucket that is in the same region as the model package.

          • ProductId (string) --

            The Amazon Web Services Marketplace product ID of the model package.

          • Environment (dict) --

            The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

            • (string) --

              • (string) --

          • ModelInput (dict) --

            A structure with Model Input details.

            • DataInputConfig (string) --

              The input configuration object for the model.

          • Framework (string) --

            The machine learning framework of the model package container image.

          • FrameworkVersion (string) --

            The framework version of the Model Package Container Image.

          • NearestModelName (string) --

            The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

          • AdditionalS3DataSource (dict) --

            The additional data source that is used during inference in the Docker container for your model package.

            • S3DataType (string) --

              The data type of the additional data source that you specify for use in inference or training.

            • S3Uri (string) --

              The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

            • CompressionType (string) --

              The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

      • SupportedTransformInstanceTypes (list) --

        A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

        This parameter is required for unversioned models, and optional for versioned models.

        • (string) --

      • SupportedRealtimeInferenceInstanceTypes (list) --

        A list of the instance types that are used to generate inferences in real-time.

        This parameter is required for unversioned models, and optional for versioned models.

        • (string) --

      • SupportedContentTypes (list) --

        The supported MIME types for the input data.

        • (string) --

      • SupportedResponseMIMETypes (list) --

        The supported MIME types for the output data.

        • (string) --

    • ValidationSpecification (dict) --

      Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.

      • ValidationRole (string) --

        The IAM roles that SageMaker uses to run the training jobs.

      • ValidationProfiles (list) --

        An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

        • (dict) --

          Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.

          The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

          • ProfileName (string) --

            The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

          • TrainingJobDefinition (dict) --

            The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

            • TrainingInputMode (string) --

              The training input mode that the algorithm supports. For more information about input modes, see Algorithms .

              Pipe mode

              If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

              File mode

              If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

              You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

              For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

              FastFile mode

              If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

              FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

            • HyperParameters (dict) --

              The hyperparameters used for the training job.

              • (string) --

                • (string) --

            • InputDataConfig (list) --

              An array of Channel objects, each of which specifies an input source.

              • (dict) --

                A channel is a named input source that training algorithms can consume.

                • ChannelName (string) --

                  The name of the channel.

                • DataSource (dict) --

                  The location of the channel data.

                  • S3DataSource (dict) --

                    The S3 location of the data source that is associated with a channel.

                    • S3DataType (string) --

                      If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

                      If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

                      If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

                    • S3Uri (string) --

                      Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                      • A key name prefix might look like this: s3://bucketname/exampleprefix

                      • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.

                      Your input bucket must be located in same Amazon Web Services region as your training job.

                    • S3DataDistributionType (string) --

                      If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

                      If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

                      Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

                      In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

                    • AttributeNames (list) --

                      A list of one or more attribute names to use that are found in a specified augmented manifest file.

                      • (string) --

                    • InstanceGroupNames (list) --

                      A list of names of instance groups that get data from the S3 data source.

                      • (string) --

                  • FileSystemDataSource (dict) --

                    The file system that is associated with a channel.

                    • FileSystemId (string) --

                      The file system id.

                    • FileSystemAccessMode (string) --

                      The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

                    • FileSystemType (string) --

                      The file system type.

                    • DirectoryPath (string) --

                      The full path to the directory to associate with the channel.

                • ContentType (string) --

                  The MIME type of the data.

                • CompressionType (string) --

                  If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

                • RecordWrapperType (string) --

                  Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .

                  In File mode, leave this field unset or set it to None.

                • InputMode (string) --

                  (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

                  To use a model for incremental training, choose File input model.

                • ShuffleConfig (dict) --

                  A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

                  For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

                  • Seed (integer) --

                    Determines the shuffling order in ShuffleConfig value.

            • OutputDataConfig (dict) --

              the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

              • KmsKeyId (string) --

                The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

                • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

                • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

                • // KMS Key Alias "alias/ExampleAlias"

                • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

                If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

                The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

              • S3OutputPath (string) --

                Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

              • CompressionType (string) --

                The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

            • ResourceConfig (dict) --

              The resources, including the ML compute instances and ML storage volumes, to use for model training.

              • InstanceType (string) --

                The ML compute instance type.

                Note

                SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

                Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

                • US East (N. Virginia) (us-east-1)

                • US West (Oregon) (us-west-2)

                To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

              • InstanceCount (integer) --

                The number of ML compute instances to use. For distributed training, provide a value greater than 1.

              • VolumeSizeInGB (integer) --

                The size of the ML storage volume that you want to provision.

                ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

                When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

                When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

                To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .

                To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .

              • VolumeKmsKeyId (string) --

                The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

                Note

                Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

                For a list of instance types that support local instance storage, see Instance Store Volumes .

                For more information about local instance storage encryption, see SSD Instance Store Volumes .

                The VolumeKmsKeyId can be in any of the following formats:

                • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

                • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

              • KeepAlivePeriodInSeconds (integer) --

                The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

              • InstanceGroups (list) --

                The configuration of a heterogeneous cluster in JSON format.

                • (dict) --

                  Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

                  • InstanceType (string) --

                    Specifies the instance type of the instance group.

                  • InstanceCount (integer) --

                    Specifies the number of instances of the instance group.

                  • InstanceGroupName (string) --

                    Specifies the name of the instance group.

            • StoppingCondition (dict) --

              Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

              To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

              • MaxRuntimeInSeconds (integer) --

                The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

                For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

                For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

                The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

              • MaxWaitTimeInSeconds (integer) --

                The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

                When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

              • MaxPendingTimeInSeconds (integer) --

                The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

          • TransformJobDefinition (dict) --

            The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

            • MaxConcurrentTransforms (integer) --

              The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

            • MaxPayloadInMB (integer) --

              The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

            • BatchStrategy (string) --

              A string that determines the number of records included in a single mini-batch.

              SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

            • Environment (dict) --

              The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

              • (string) --

                • (string) --

            • TransformInput (dict) --

              A description of the input source and the way the transform job consumes it.

              • DataSource (dict) --

                Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

                • S3DataSource (dict) --

                  The S3 location of the data source that is associated with a channel.

                  • S3DataType (string) --

                    If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

                    If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

                    The following values are compatible: ManifestFile , S3Prefix

                    The following value is not compatible: AugmentedManifestFile

                  • S3Uri (string) --

                    Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                    • A key name prefix might look like this: s3://bucketname/exampleprefix .

                    • A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

              • ContentType (string) --

                The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

              • CompressionType (string) --

                If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .

              • SplitType (string) --

                The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:

                • RecordIO

                • TFRecord

                When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.

                Note

                Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .

                For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.

            • TransformOutput (dict) --

              Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

              • S3OutputPath (string) --

                The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .

                For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

              • Accept (string) --

                The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

              • AssembleWith (string) --

                Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .

              • KmsKeyId (string) --

                The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

                • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

                • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

                • Alias name: alias/ExampleAlias

                • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

                If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

                The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

            • TransformResources (dict) --

              Identifies the ML compute instances for the transform job.

              • InstanceType (string) --

                The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.

              • InstanceCount (integer) --

                The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .

              • VolumeKmsKeyId (string) --

                The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.

                Note

                Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

                For a list of instance types that support local instance storage, see Instance Store Volumes .

                For more information about local instance storage encryption, see SSD Instance Store Volumes .

                The VolumeKmsKeyId can be any of the following formats:

                • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

                • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

                • Alias name: alias/ExampleAlias

                • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

    • AlgorithmStatus (string) --

      The current status of the algorithm.

    • AlgorithmStatusDetails (dict) --

      Details about the current status of the algorithm.

      • ValidationStatuses (list) --

        The status of algorithm validation.

        • (dict) --

          Represents the overall status of an algorithm.

          • Name (string) --

            The name of the algorithm for which the overall status is being reported.

          • Status (string) --

            The current status.

          • FailureReason (string) --

            if the overall status is Failed , the reason for the failure.

      • ImageScanStatuses (list) --

        The status of the scan of the algorithm's Docker image container.

        • (dict) --

          Represents the overall status of an algorithm.

          • Name (string) --

            The name of the algorithm for which the overall status is being reported.

          • Status (string) --

            The current status.

          • FailureReason (string) --

            if the overall status is Failed , the reason for the failure.

    • ProductId (string) --

      The product identifier of the algorithm.

    • CertifyForMarketplace (boolean) --

      Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

DescribeApp (updated) Link ¶
Changes (both)
{'AppType': {'Canvas',
             'DatasetManager',
             'DetailedProfiler',
             'Local',
             'RSession',
             'SageMakerLite',
             'Savitur',
             'VSCode'}}

Describes the app.

See also: AWS API Documentation

Request Syntax

client.describe_app(
    DomainId='string',
    UserProfileName='string',
    SpaceName='string',
    AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
    AppName='string'
)
type DomainId

string

param DomainId

[REQUIRED]

The domain ID.

type UserProfileName

string

param UserProfileName

The user profile name. If this value is not set, then SpaceName must be set.

type SpaceName

string

param SpaceName

The name of the space.

type AppType

string

param AppType

[REQUIRED]

The type of app.

type AppName

string

param AppName

[REQUIRED]

The name of the app.

rtype

dict

returns

Response Syntax

{
    'AppArn': 'string',
    'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
    'AppName': 'string',
    'DomainId': 'string',
    'UserProfileName': 'string',
    'SpaceName': 'string',
    'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
    'LastHealthCheckTimestamp': datetime(2015, 1, 1),
    'LastUserActivityTimestamp': datetime(2015, 1, 1),
    'CreationTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'ResourceSpec': {
        'SageMakerImageArn': 'string',
        'SageMakerImageVersionArn': 'string',
        'SageMakerImageVersionAlias': 'string',
        'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
        'LifecycleConfigArn': 'string'
    }
}

Response Structure

  • (dict) --

    • AppArn (string) --

      The Amazon Resource Name (ARN) of the app.

    • AppType (string) --

      The type of app.

    • AppName (string) --

      The name of the app.

    • DomainId (string) --

      The domain ID.

    • UserProfileName (string) --

      The user profile name.

    • SpaceName (string) --

      The name of the space. If this value is not set, then UserProfileName must be set.

    • Status (string) --

      The status.

    • LastHealthCheckTimestamp (datetime) --

      The timestamp of the last health check.

    • LastUserActivityTimestamp (datetime) --

      The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp .

    • CreationTime (datetime) --

      The creation time.

    • FailureReason (string) --

      The failure reason.

    • ResourceSpec (dict) --

      The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

      • SageMakerImageArn (string) --

        The ARN of the SageMaker image that the image version belongs to.

      • SageMakerImageVersionArn (string) --

        The ARN of the image version created on the instance.

      • SageMakerImageVersionAlias (string) --

        The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.

      • InstanceType (string) --

        The instance type that the image version runs on.

        Note

        JupyterServer apps only support the system value.

        For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

      • LifecycleConfigArn (string) --

        The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

DescribeAppImageConfig (updated) Link ¶
Changes (response)
{'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
                                                   'DefaultUid': 'integer',
                                                   'MountPath': 'string'}}}

Describes an AppImageConfig.

See also: AWS API Documentation

Request Syntax

client.describe_app_image_config(
    AppImageConfigName='string'
)
type AppImageConfigName

string

param AppImageConfigName

[REQUIRED]

The name of the AppImageConfig to describe.

rtype

dict

returns

Response Syntax

{
    'AppImageConfigArn': 'string',
    'AppImageConfigName': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'KernelGatewayImageConfig': {
        'KernelSpecs': [
            {
                'Name': 'string',
                'DisplayName': 'string'
            },
        ],
        'FileSystemConfig': {
            'MountPath': 'string',
            'DefaultUid': 123,
            'DefaultGid': 123
        }
    },
    'JupyterLabAppImageConfig': {
        'FileSystemConfig': {
            'MountPath': 'string',
            'DefaultUid': 123,
            'DefaultGid': 123
        },
        'ContainerConfig': {
            'ContainerArguments': [
                'string',
            ],
            'ContainerEntrypoint': [
                'string',
            ],
            'ContainerEnvironmentVariables': {
                'string': 'string'
            }
        }
    }
}

Response Structure

  • (dict) --

    • AppImageConfigArn (string) --

      The Amazon Resource Name (ARN) of the AppImageConfig.

    • AppImageConfigName (string) --

      The name of the AppImageConfig.

    • CreationTime (datetime) --

      When the AppImageConfig was created.

    • LastModifiedTime (datetime) --

      When the AppImageConfig was last modified.

    • KernelGatewayImageConfig (dict) --

      The configuration of a KernelGateway app.

      • KernelSpecs (list) --

        The specification of the Jupyter kernels in the image.

        • (dict) --

          The specification of a Jupyter kernel.

          • Name (string) --

            The name of the Jupyter kernel in the image. This value is case sensitive.

          • DisplayName (string) --

            The display name of the kernel.

      • FileSystemConfig (dict) --

        The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

        • MountPath (string) --

          The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .

        • DefaultUid (integer) --

          The default POSIX user ID (UID). If not specified, defaults to 1000 .

        • DefaultGid (integer) --

          The default POSIX group ID (GID). If not specified, defaults to 100 .

    • JupyterLabAppImageConfig (dict) --

      The configuration of the JupyterLab app.

      • FileSystemConfig (dict) --

        The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

        • MountPath (string) --

          The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .

        • DefaultUid (integer) --

          The default POSIX user ID (UID). If not specified, defaults to 1000 .

        • DefaultGid (integer) --

          The default POSIX group ID (GID). If not specified, defaults to 100 .

      • ContainerConfig (dict) --

        The configuration used to run the application image container.

        • ContainerArguments (list) --

          The arguments for the container when you're running the application.

          • (string) --

        • ContainerEntrypoint (list) --

          The entrypoint used to run the application in the container.

          • (string) --

        • ContainerEnvironmentVariables (dict) --

          The environment variables to set in the container

          • (string) --

            • (string) --

DescribeAutoMLJobV2 (updated) Link ¶
Changes (response)
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'ModelAccessConfig': {'AcceptEula': 'boolean'}}}}

Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob .

See also: AWS API Documentation

Request Syntax

client.describe_auto_ml_job_v2(
    AutoMLJobName='string'
)
type AutoMLJobName

string

param AutoMLJobName

[REQUIRED]

Requests information about an AutoML job V2 using its unique name.

rtype

dict

returns

Response Syntax

{
    'AutoMLJobName': 'string',
    'AutoMLJobArn': 'string',
    'AutoMLJobInputDataConfig': [
        {
            'ChannelType': 'training'|'validation',
            'ContentType': 'string',
            'CompressionType': 'None'|'Gzip',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            }
        },
    ],
    'OutputDataConfig': {
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    'RoleArn': 'string',
    'AutoMLJobObjective': {
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
    },
    'AutoMLProblemTypeConfig': {
        'ImageClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            }
        },
        'TextClassificationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ContentColumn': 'string',
            'TargetLabelColumn': 'string'
        },
        'TimeSeriesForecastingJobConfig': {
            'FeatureSpecificationS3Uri': 'string',
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'ForecastFrequency': 'string',
            'ForecastHorizon': 123,
            'ForecastQuantiles': [
                'string',
            ],
            'Transformations': {
                'Filling': {
                    'string': {
                        'string': 'string'
                    }
                },
                'Aggregation': {
                    'string': 'sum'|'avg'|'first'|'min'|'max'
                }
            },
            'TimeSeriesConfig': {
                'TargetAttributeName': 'string',
                'TimestampAttributeName': 'string',
                'ItemIdentifierAttributeName': 'string',
                'GroupingAttributeNames': [
                    'string',
                ]
            },
            'HolidayConfig': [
                {
                    'CountryCode': 'string'
                },
            ]
        },
        'TabularJobConfig': {
            'CandidateGenerationConfig': {
                'AlgorithmsConfig': [
                    {
                        'AutoMLAlgorithms': [
                            'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
                        ]
                    },
                ]
            },
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'FeatureSpecificationS3Uri': 'string',
            'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
            'GenerateCandidateDefinitionsOnly': True|False,
            'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
            'TargetAttributeName': 'string',
            'SampleWeightAttributeName': 'string'
        },
        'TextGenerationJobConfig': {
            'CompletionCriteria': {
                'MaxCandidates': 123,
                'MaxRuntimePerTrainingJobInSeconds': 123,
                'MaxAutoMLJobRuntimeInSeconds': 123
            },
            'BaseModelName': 'string',
            'TextGenerationHyperParameters': {
                'string': 'string'
            },
            'ModelAccessConfig': {
                'AcceptEula': True|False
            }
        }
    },
    'AutoMLProblemTypeConfigName': 'ImageClassification'|'TextClassification'|'TimeSeriesForecasting'|'Tabular'|'TextGeneration',
    'CreationTime': datetime(2015, 1, 1),
    'EndTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'PartialFailureReasons': [
        {
            'PartialFailureMessage': 'string'
        },
    ],
    'BestCandidate': {
        'CandidateName': 'string',
        'FinalAutoMLJobObjectiveMetric': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
            'Value': ...,
            'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
        'CandidateSteps': [
            {
                'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
                'CandidateStepArn': 'string',
                'CandidateStepName': 'string'
            },
        ],
        'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
        'InferenceContainers': [
            {
                'Image': 'string',
                'ModelDataUrl': 'string',
                'Environment': {
                    'string': 'string'
                }
            },
        ],
        'CreationTime': datetime(2015, 1, 1),
        'EndTime': datetime(2015, 1, 1),
        'LastModifiedTime': datetime(2015, 1, 1),
        'FailureReason': 'string',
        'CandidateProperties': {
            'CandidateArtifactLocations': {
                'Explainability': 'string',
                'ModelInsights': 'string',
                'BacktestResults': 'string'
            },
            'CandidateMetrics': [
                {
                    'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
                    'Value': ...,
                    'Set': 'Train'|'Validation'|'Test',
                    'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss'
                },
            ]
        },
        'InferenceContainerDefinitions': {
            'string': [
                {
                    'Image': 'string',
                    'ModelDataUrl': 'string',
                    'Environment': {
                        'string': 'string'
                    }
                },
            ]
        }
    },
    'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
    'AutoMLJobSecondaryStatus': 'Starting'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'GeneratingExplainabilityReport'|'TrainingModels'|'PreTraining',
    'AutoMLJobArtifacts': {
        'CandidateDefinitionNotebookLocation': 'string',
        'DataExplorationNotebookLocation': 'string'
    },
    'ResolvedAttributes': {
        'AutoMLJobObjective': {
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'CompletionCriteria': {
            'MaxCandidates': 123,
            'MaxRuntimePerTrainingJobInSeconds': 123,
            'MaxAutoMLJobRuntimeInSeconds': 123
        },
        'AutoMLProblemTypeResolvedAttributes': {
            'TabularResolvedAttributes': {
                'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression'
            },
            'TextGenerationResolvedAttributes': {
                'BaseModelName': 'string'
            }
        }
    },
    'ModelDeployConfig': {
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    'ModelDeployResult': {
        'EndpointName': 'string'
    },
    'DataSplitConfig': {
        'ValidationFraction': ...
    },
    'SecurityConfig': {
        'VolumeKmsKeyId': 'string',
        'EnableInterContainerTrafficEncryption': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    }
}

Response Structure

  • (dict) --

    • AutoMLJobName (string) --

      Returns the name of the AutoML job V2.

    • AutoMLJobArn (string) --

      Returns the Amazon Resource Name (ARN) of the AutoML job V2.

    • AutoMLJobInputDataConfig (list) --

      Returns an array of channel objects describing the input data and their location.

      • (dict) --

        A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).

        • ChannelType (string) --

          The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType

          Note

          The type of channel defaults to training for the time-series forecasting problem type.

        • ContentType (string) --

          The content type of the data from the input source. The following are the allowed content types for different problems:

          • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For image classification: image/png , image/jpeg , or image/* . The default value is image/* .

          • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

          • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

        • CompressionType (string) --

          The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .

        • DataSource (dict) --

          The data source for an AutoML channel (Required).

          • S3DataSource (dict) --

            The Amazon S3 location of the input data.

            • S3DataType (string) --

              The data type.

              • If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

              • If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

              • If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .

            • S3Uri (string) --

              The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

    • OutputDataConfig (dict) --

      Returns the job's output data config.

      • KmsKeyId (string) --

        The Key Management Service (KMS) encryption key ID.

      • S3OutputPath (string) --

        The Amazon S3 output path. Must be 128 characters or less.

    • RoleArn (string) --

      The ARN of the Identity and Access Management role that has read permission to the input data location and write permission to the output data location in Amazon S3.

    • AutoMLJobObjective (dict) --

      Returns the job's objective.

      • MetricName (string) --

        The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

        The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

        • For tabular problem types:

          • List of available metrics:

            • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

            • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

            • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

          For a description of each metric, see Autopilot metrics for classification and regression .

          • Default objective metrics:

            • Regression: MSE .

            • Binary classification: F1 .

            • Multiclass classification: Accuracy .

        • For image or text classification problem types:

        • For time-series forecasting problem types:

        • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

    • AutoMLProblemTypeConfig (dict) --

      Returns the configuration settings of the problem type set for the AutoML job V2.

      • ImageClassificationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the image classification problem type.

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • TextClassificationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the text classification problem type.

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • ContentColumn (string) --

          The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

        • TargetLabelColumn (string) --

          The name of the column used to provide the class labels. It should not be same as the content column.

      • TimeSeriesForecastingJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

        • FeatureSpecificationS3Uri (string) --

          A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .

          You can input FeatureAttributeNames (optional) in JSON format as shown below:

          { "FeatureAttributeNames":["col1", "col2", ...] } .

          You can also specify the data type of the feature (optional) in the format shown below:

          { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

          Autopilot supports the following data types: numeric , categorical , text , and datetime .

          Note

          These column keys must not include any column set in TimeSeriesConfig .

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • ForecastFrequency (string) --

          The frequency of predictions in a forecast.

          Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .

          The valid values for each frequency are the following:

          • Minute - 1-59

          • Hour - 1-23

          • Day - 1-6

          • Week - 1-4

          • Month - 1-11

          • Year - 1

        • ForecastHorizon (integer) --

          The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.

        • ForecastQuantiles (list) --

          The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.

          • (string) --

        • Transformations (dict) --

          The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.

          • Filling (dict) --

            A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.

            The supported filling methods and their corresponding options are:

            • frontfill : none (Supported only for target column)

            • middlefill : zero , value , median , mean , min , max

            • backfill : zero , value , median , mean , min , max

            • futurefill : zero , value , median , mean , min , max

            To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .

            • (string) --

              • (dict) --

                • (string) --

                  • (string) --

          • Aggregation (dict) --

            A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.

            The supported aggregation methods are sum (default), avg , first , min , max .

            Note

            Aggregation is only supported for the target column.

            • (string) --

              • (string) --

        • TimeSeriesConfig (dict) --

          The collection of components that defines the time-series.

          • TargetAttributeName (string) --

            The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.

          • TimestampAttributeName (string) --

            The name of the column indicating a point in time at which the target value of a given item is recorded.

          • ItemIdentifierAttributeName (string) --

            The name of the column that represents the set of item identifiers for which you want to predict the target value.

          • GroupingAttributeNames (list) --

            A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.

            • (string) --

        • HolidayConfig (list) --

          The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.

          • (dict) --

            Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

            • CountryCode (string) --

              The country code for the holiday calendar.

              For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.

      • TabularJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

        • CandidateGenerationConfig (dict) --

          The configuration information of how model candidates are generated.

          • AlgorithmsConfig (list) --

            Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.

            The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .

            • AlgorithmsConfig should not be set in AUTO training mode.

            • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

            For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .

            For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

            • (dict) --

              The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.

              • AutoMLAlgorithms (list) --

                The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.

                Note

                Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.

                • In ENSEMBLING mode:

                  • "catboost"

                  • "extra-trees"

                  • "fastai"

                  • "lightgbm"

                  • "linear-learner"

                  • "nn-torch"

                  • "randomforest"

                  • "xgboost"

                • In HYPERPARAMETER_TUNING mode:

                  • "linear-learner"

                  • "mlp"

                  • "xgboost"

                • (string) --

        • CompletionCriteria (dict) --

          How long a job is allowed to run, or how many candidates a job is allowed to generate.

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • FeatureSpecificationS3Uri (string) --

          A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

          { "FeatureAttributeNames":["col1", "col2", ...] } .

          You can also specify the data type of the feature (optional) in the format shown below:

          { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

          Note

          These column keys may not include the target column.

          In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .

          If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

          If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

          The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

        • Mode (string) --

          The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

          The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

          The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

        • GenerateCandidateDefinitionsOnly (boolean) --

          Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

        • ProblemType (string) --

          The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .

          Note

          You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

        • TargetAttributeName (string) --

          The name of the target variable in supervised learning, usually represented by 'y'.

        • SampleWeightAttributeName (string) --

          If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .

          Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

          Support for sample weights is available in Ensembling mode only.

      • TextGenerationJobConfig (dict) --

        Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.

        Note

        The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .

        • CompletionCriteria (dict) --

          How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).

          • MaxCandidates (integer) --

            The maximum number of times a training job is allowed to run.

            For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

          • MaxRuntimePerTrainingJobInSeconds (integer) --

            The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

            For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

            For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

          • MaxAutoMLJobRuntimeInSeconds (integer) --

            The maximum runtime, in seconds, an AutoML job has to complete.

            If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

        • BaseModelName (string) --

          The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .

        • TextGenerationHyperParameters (dict) --

          The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .

          • "epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".

          • "batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".

          • "learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".

          • "learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".

          Here is an example where all four hyperparameters are configured.

          { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }

          • (string) --

            • (string) --

        • ModelAccessConfig (dict) --

          The access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . For more information, see End-user license agreements .

          • AcceptEula (boolean) --

            Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

    • AutoMLProblemTypeConfigName (string) --

      Returns the name of the problem type configuration set for the AutoML job V2.

    • CreationTime (datetime) --

      Returns the creation time of the AutoML job V2.

    • EndTime (datetime) --

      Returns the end time of the AutoML job V2.

    • LastModifiedTime (datetime) --

      Returns the job's last modified time.

    • FailureReason (string) --

      Returns the reason for the failure of the AutoML job V2, when applicable.

    • PartialFailureReasons (list) --

      Returns a list of reasons for partial failures within an AutoML job V2.

      • (dict) --

        The reason for a partial failure of an AutoML job.

        • PartialFailureMessage (string) --

          The message containing the reason for a partial failure of an AutoML job.

    • BestCandidate (dict) --

      Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.

      • CandidateName (string) --

        The name of the candidate.

      • FinalAutoMLJobObjectiveMetric (dict) --

        The best candidate result from an AutoML training job.

        • Type (string) --

          The type of metric with the best result.

        • MetricName (string) --

          The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .

        • Value (float) --

          The value of the metric with the best result.

        • StandardMetricName (string) --

          The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics .

      • ObjectiveStatus (string) --

        The objective's status.

      • CandidateSteps (list) --

        Information about the candidate's steps.

        • (dict) --

          Information about the steps for a candidate and what step it is working on.

          • CandidateStepType (string) --

            Whether the candidate is at the transform, training, or processing step.

          • CandidateStepArn (string) --

            The ARN for the candidate's step.

          • CandidateStepName (string) --

            The name for the candidate's step.

      • CandidateStatus (string) --

        The candidate's status.

      • InferenceContainers (list) --

        Information about the recommended inference container definitions.

        • (dict) --

          A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

          • Image (string) --

            The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

          • ModelDataUrl (string) --

            The location of the model artifacts. For more information, see ContainerDefinition .

          • Environment (dict) --

            The environment variables to set in the container. For more information, see ContainerDefinition .

            • (string) --

              • (string) --

      • CreationTime (datetime) --

        The creation time.

      • EndTime (datetime) --

        The end time.

      • LastModifiedTime (datetime) --

        The last modified time.

      • FailureReason (string) --

        The failure reason.

      • CandidateProperties (dict) --

        The properties of an AutoML candidate job.

        • CandidateArtifactLocations (dict) --

          The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

          • Explainability (string) --

            The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

          • ModelInsights (string) --

            The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

          • BacktestResults (string) --

            The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.

        • CandidateMetrics (list) --

          Information about the candidate metrics for an AutoML job.

          • (dict) --

            Information about the metric for a candidate produced by an AutoML job.

      • InferenceContainerDefinitions (dict) --

        The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2 ) related to image or text classification problem types only.

        • (string) --

          Processing unit for an inference container. Currently Autopilot only supports CPU or GPU .

          • (list) --

            Information about the recommended inference container definitions.

            • (dict) --

              A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

              • Image (string) --

                The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

              • ModelDataUrl (string) --

                The location of the model artifacts. For more information, see ContainerDefinition .

              • Environment (dict) --

                The environment variables to set in the container. For more information, see ContainerDefinition .

                • (string) --

                  • (string) --

    • AutoMLJobStatus (string) --

      Returns the status of the AutoML job V2.

    • AutoMLJobSecondaryStatus (string) --

      Returns the secondary status of the AutoML job V2.

    • AutoMLJobArtifacts (dict) --

      The artifacts that are generated during an AutoML job.

      • CandidateDefinitionNotebookLocation (string) --

        The URL of the notebook location.

      • DataExplorationNotebookLocation (string) --

        The URL of the notebook location.

    • ResolvedAttributes (dict) --

      Returns the resolved attributes used by the AutoML job V2.

      • AutoMLJobObjective (dict) --

        Specifies a metric to minimize or maximize as the objective of an AutoML job.

        • MetricName (string) --

          The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

          The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

          • For tabular problem types:

            • List of available metrics:

              • Regression: InferenceLatency , MAE , MSE , R2 , RMSE

              • Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall

              • Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro

            For a description of each metric, see Autopilot metrics for classification and regression .

            • Default objective metrics:

              • Regression: MSE .

              • Binary classification: F1 .

              • Multiclass classification: Accuracy .

          • For image or text classification problem types:

          • For time-series forecasting problem types:

          • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .

      • CompletionCriteria (dict) --

        How long a job is allowed to run, or how many candidates a job is allowed to generate.

        • MaxCandidates (integer) --

          The maximum number of times a training job is allowed to run.

          For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • MaxRuntimePerTrainingJobInSeconds (integer) --

          The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

          For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • MaxAutoMLJobRuntimeInSeconds (integer) --

          The maximum runtime, in seconds, an AutoML job has to complete.

          If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

      • AutoMLProblemTypeResolvedAttributes (dict) --

        Defines the resolved attributes specific to a problem type.

        • TabularResolvedAttributes (dict) --

          The resolved attributes for the tabular problem type.

          • ProblemType (string) --

            The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see Amazon SageMaker Autopilot problem types .

        • TextGenerationResolvedAttributes (dict) --

          The resolved attributes for the text generation problem type.

          • BaseModelName (string) --

            The name of the base model to fine-tune.

    • ModelDeployConfig (dict) --

      Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

      • AutoGenerateEndpointName (boolean) --

        Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

        Note

        If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

      • EndpointName (string) --

        Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

        Note

        Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

    • ModelDeployResult (dict) --

      Provides information about endpoint for the model deployment.

      • EndpointName (string) --

        The name of the endpoint to which the model has been deployed.

        Note

        If model deployment fails, this field is omitted from the response.

    • DataSplitConfig (dict) --

      Returns the configuration settings of how the data are split into train and validation datasets.

      • ValidationFraction (float) --

        The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

    • SecurityConfig (dict) --

      Returns the security configuration for traffic encryption or Amazon VPC settings.

      • VolumeKmsKeyId (string) --

        The key used to encrypt stored data.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to use traffic encryption between the container layers.

      • VpcConfig (dict) --

        The VPC configuration.

        • SecurityGroupIds (list) --

          The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --

        • Subnets (list) --

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

          • (string) --

DescribeCompilationJob (updated) Link ¶
Changes (response)
{'OutputConfig': {'TargetDevice': {'ml_c6g', 'ml_m6g', 'rasp4b'}}}

Returns information about a model compilation job.

To create a model compilation job, use CreateCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .

See also: AWS API Documentation

Request Syntax

client.describe_compilation_job(
    CompilationJobName='string'
)
type CompilationJobName

string

param CompilationJobName

[REQUIRED]

The name of the model compilation job that you want information about.

rtype

dict

returns

Response Syntax

{
    'CompilationJobName': 'string',
    'CompilationJobArn': 'string',
    'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
    'CompilationStartTime': datetime(2015, 1, 1),
    'CompilationEndTime': datetime(2015, 1, 1),
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123,
        'MaxPendingTimeInSeconds': 123
    },
    'InferenceImage': 'string',
    'ModelPackageVersionArn': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'ModelArtifacts': {
        'S3ModelArtifacts': 'string'
    },
    'ModelDigests': {
        'ArtifactDigest': 'string'
    },
    'RoleArn': 'string',
    'InputConfig': {
        'S3Uri': 'string',
        'DataInputConfig': 'string',
        'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
        'FrameworkVersion': 'string'
    },
    'OutputConfig': {
        'S3OutputLocation': 'string',
        'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
        'TargetPlatform': {
            'Os': 'ANDROID'|'LINUX',
            'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
            'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA'
        },
        'CompilerOptions': 'string',
        'KmsKeyId': 'string'
    },
    'VpcConfig': {
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    'DerivedInformation': {
        'DerivedDataInputConfig': 'string'
    }
}

Response Structure

  • (dict) --

    • CompilationJobName (string) --

      The name of the model compilation job.

    • CompilationJobArn (string) --

      The Amazon Resource Name (ARN) of the model compilation job.

    • CompilationJobStatus (string) --

      The status of the model compilation job.

    • CompilationStartTime (datetime) --

      The time when the model compilation job started the CompilationJob instances.

      You are billed for the time between this timestamp and the timestamp in the CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.

    • CompilationEndTime (datetime) --

      The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.

    • StoppingCondition (dict) --

      Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

      • MaxRuntimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

        For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

        For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

        The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

      • MaxWaitTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

        When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

      • MaxPendingTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

    • InferenceImage (string) --

      The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.

    • ModelPackageVersionArn (string) --

      The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.

    • CreationTime (datetime) --

      The time that the model compilation job was created.

    • LastModifiedTime (datetime) --

      The time that the status of the model compilation job was last modified.

    • FailureReason (string) --

      If a model compilation job failed, the reason it failed.

    • ModelArtifacts (dict) --

      Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

      • S3ModelArtifacts (string) --

        The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .

    • ModelDigests (dict) --

      Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.

      • ArtifactDigest (string) --

        Provides a hash value that uniquely identifies the stored model artifacts.

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

    • InputConfig (dict) --

      Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

      • S3Uri (string) --

        The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

      • DataInputConfig (string) --

        Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.

        • TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

          • Examples for one input:

            • If using the console, {"input":[1,1024,1024,3]}

            • If using the CLI, {\"input\":[1,1024,1024,3]}

          • Examples for two inputs:

            • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

            • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}

        • KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.

          • Examples for one input:

            • If using the console, {"input_1":[1,3,224,224]}

            • If using the CLI, {\"input_1\":[1,3,224,224]}

          • Examples for two inputs:

            • If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

            • If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}

        • MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

          • Examples for one input:

            • If using the console, {"data":[1,3,1024,1024]}

            • If using the CLI, {\"data\":[1,3,1024,1024]}

          • Examples for two inputs:

            • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

            • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}

        • PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

          • Examples for one input in dictionary format:

            • If using the console, {"input0":[1,3,224,224]}

            • If using the CLI, {\"input0\":[1,3,224,224]}

          • Example for one input in list format: [[1,3,224,224]]

          • Examples for two inputs in dictionary format:

            • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

            • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}

          • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

        • XGBOOST : input data name and shape are not needed.

        DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):

        • shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:

          • Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}

          • Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}

        • default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}

        • type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .

        • bias : If the input type is an Image, you need to provide the bias vector.

        • scale : If the input type is an Image, you need to provide a scale factor.

        CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:

        • Tensor type input:

          • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}

        • Tensor type input without input name (PyTorch):

          • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]

        • Image type input:

          • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}

          • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

        • Image type input without input name (PyTorch):

          • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]

          • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

        Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .

      • Framework (string) --

        Identifies the framework in which the model was trained. For example: TENSORFLOW.

      • FrameworkVersion (string) --

        Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

        For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks .

    • OutputConfig (dict) --

      Information about the output location for the compiled model and the target device that the model runs on.

      • S3OutputLocation (string) --

        Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

      • TargetDevice (string) --

        Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .

        Note

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

      • TargetPlatform (dict) --

        Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}

        • Os (string) --

          Specifies a target platform OS.

          • LINUX : Linux-based operating systems.

          • ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}

        • Arch (string) --

          Specifies a target platform architecture.

          • X86_64 : 64-bit version of the x86 instruction set.

          • X86 : 32-bit version of the x86 instruction set.

          • ARM64 : ARMv8 64-bit CPU.

          • ARM_EABIHF : ARMv7 32-bit, Hard Float.

          • ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.

        • Accelerator (string) --

          Specifies a target platform accelerator (optional).

          • NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options

          • MALI : ARM Mali graphics processor

          • INTEL_GRAPHICS : Integrated Intel graphics

      • CompilerOptions (string) --

        Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE : Specifies the data type for the input. When compiling for ml_* (except for ml_inf ) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32" .

          • int64: Use either "int64" or "long" .

        For example, {"dtype" : "float32"} .

        • CPU : Compilation for CPU supports the following compiler options.

          • mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM : Details of ARM CPU compilations.

          • NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code : Specifies the targeted architecture.

          • trt-ver : Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver : Specifies the CUDA version in x.y format.

        For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID : Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .

          • mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI Reference Guide .

        • CoreML : Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:

          • class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.

        • EIA : Compilation for the Elastic Inference Accelerator supports the following compiler options:

          • precision_mode : Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32" . Default is "FP32" .

          • signature_def_key : Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.

          • output_names : Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names .

        For example: {"precision_mode": "FP32", "output_names": ["output:0"]}

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

        The KmsKeyId can be any of the following formats:

        • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

        • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

        • Alias name: alias/ExampleAlias

        • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

    • VpcConfig (dict) --

      A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .

      • SecurityGroupIds (list) --

        The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

        • (string) --

      • Subnets (list) --

        The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.

        • (string) --

    • DerivedInformation (dict) --

      Information that SageMaker Neo automatically derived about the model.

      • DerivedDataInputConfig (string) --

        The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job.

DescribeEndpoint (updated) Link ¶
Changes (response)
{'PendingDeploymentSummary': {'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                                                      'ml.c7i.16xlarge',
                                                                      'ml.c7i.24xlarge',
                                                                      'ml.c7i.2xlarge',
                                                                      'ml.c7i.48xlarge',
                                                                      'ml.c7i.4xlarge',
                                                                      'ml.c7i.8xlarge',
                                                                      'ml.c7i.large',
                                                                      'ml.c7i.xlarge',
                                                                      'ml.dl1.24xlarge',
                                                                      'ml.m7i.12xlarge',
                                                                      'ml.m7i.16xlarge',
                                                                      'ml.m7i.24xlarge',
                                                                      'ml.m7i.2xlarge',
                                                                      'ml.m7i.48xlarge',
                                                                      'ml.m7i.4xlarge',
                                                                      'ml.m7i.8xlarge',
                                                                      'ml.m7i.large',
                                                                      'ml.m7i.xlarge',
                                                                      'ml.r7i.12xlarge',
                                                                      'ml.r7i.16xlarge',
                                                                      'ml.r7i.24xlarge',
                                                                      'ml.r7i.2xlarge',
                                                                      'ml.r7i.48xlarge',
                                                                      'ml.r7i.4xlarge',
                                                                      'ml.r7i.8xlarge',
                                                                      'ml.r7i.large',
                                                                      'ml.r7i.xlarge',
                                                                      'ml.trn1n.32xlarge'}},
                              'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                                                            'ml.c7i.16xlarge',
                                                                            'ml.c7i.24xlarge',
                                                                            'ml.c7i.2xlarge',
                                                                            'ml.c7i.48xlarge',
                                                                            'ml.c7i.4xlarge',
                                                                            'ml.c7i.8xlarge',
                                                                            'ml.c7i.large',
                                                                            'ml.c7i.xlarge',
                                                                            'ml.dl1.24xlarge',
                                                                            'ml.m7i.12xlarge',
                                                                            'ml.m7i.16xlarge',
                                                                            'ml.m7i.24xlarge',
                                                                            'ml.m7i.2xlarge',
                                                                            'ml.m7i.48xlarge',
                                                                            'ml.m7i.4xlarge',
                                                                            'ml.m7i.8xlarge',
                                                                            'ml.m7i.large',
                                                                            'ml.m7i.xlarge',
                                                                            'ml.r7i.12xlarge',
                                                                            'ml.r7i.16xlarge',
                                                                            'ml.r7i.24xlarge',
                                                                            'ml.r7i.2xlarge',
                                                                            'ml.r7i.48xlarge',
                                                                            'ml.r7i.4xlarge',
                                                                            'ml.r7i.8xlarge',
                                                                            'ml.r7i.large',
                                                                            'ml.r7i.xlarge',
                                                                            'ml.trn1n.32xlarge'}}}}

Returns the description of an endpoint.

See also: AWS API Documentation

Request Syntax

client.describe_endpoint(
    EndpointName='string'
)
type EndpointName

string

param EndpointName

[REQUIRED]

The name of the endpoint.

rtype

dict

returns

Response Syntax

{
    'EndpointName': 'string',
    'EndpointArn': 'string',
    'EndpointConfigName': 'string',
    'ProductionVariants': [
        {
            'VariantName': 'string',
            'DeployedImages': [
                {
                    'SpecifiedImage': 'string',
                    'ResolvedImage': 'string',
                    'ResolutionTime': datetime(2015, 1, 1)
                },
            ],
            'CurrentWeight': ...,
            'DesiredWeight': ...,
            'CurrentInstanceCount': 123,
            'DesiredInstanceCount': 123,
            'VariantStatus': [
                {
                    'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
                    'StatusMessage': 'string',
                    'StartTime': datetime(2015, 1, 1)
                },
            ],
            'CurrentServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'DesiredServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    'DataCaptureConfig': {
        'EnableCapture': True|False,
        'CaptureStatus': 'Started'|'Stopped',
        'CurrentSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string'
    },
    'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed',
    'FailureReason': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'LastDeploymentConfig': {
        'BlueGreenUpdatePolicy': {
            'TrafficRoutingConfiguration': {
                'Type': 'ALL_AT_ONCE'|'CANARY'|'LINEAR',
                'WaitIntervalInSeconds': 123,
                'CanarySize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                },
                'LinearStepSize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                }
            },
            'TerminationWaitInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123
        },
        'RollingUpdatePolicy': {
            'MaximumBatchSize': {
                'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            },
            'WaitIntervalInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123,
            'RollbackMaximumBatchSize': {
                'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                'Value': 123
            }
        },
        'AutoRollbackConfiguration': {
            'Alarms': [
                {
                    'AlarmName': 'string'
                },
            ]
        }
    },
    'AsyncInferenceConfig': {
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    'PendingDeploymentSummary': {
        'EndpointConfigName': 'string',
        'ProductionVariants': [
            {
                'VariantName': 'string',
                'DeployedImages': [
                    {
                        'SpecifiedImage': 'string',
                        'ResolvedImage': 'string',
                        'ResolutionTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentWeight': ...,
                'DesiredWeight': ...,
                'CurrentInstanceCount': 123,
                'DesiredInstanceCount': 123,
                'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
                'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
                'VariantStatus': [
                    {
                        'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
                        'StatusMessage': 'string',
                        'StartTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentServerlessConfig': {
                    'MemorySizeInMB': 123,
                    'MaxConcurrency': 123,
                    'ProvisionedConcurrency': 123
                },
                'DesiredServerlessConfig': {
                    'MemorySizeInMB': 123,
                    'MaxConcurrency': 123,
                    'ProvisionedConcurrency': 123
                },
                'ManagedInstanceScaling': {
                    'Status': 'ENABLED'|'DISABLED',
                    'MinInstanceCount': 123,
                    'MaxInstanceCount': 123
                },
                'RoutingConfig': {
                    'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
                }
            },
        ],
        'StartTime': datetime(2015, 1, 1),
        'ShadowProductionVariants': [
            {
                'VariantName': 'string',
                'DeployedImages': [
                    {
                        'SpecifiedImage': 'string',
                        'ResolvedImage': 'string',
                        'ResolutionTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentWeight': ...,
                'DesiredWeight': ...,
                'CurrentInstanceCount': 123,
                'DesiredInstanceCount': 123,
                'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
                'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
                'VariantStatus': [
                    {
                        'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
                        'StatusMessage': 'string',
                        'StartTime': datetime(2015, 1, 1)
                    },
                ],
                'CurrentServerlessConfig': {
                    'MemorySizeInMB': 123,
                    'MaxConcurrency': 123,
                    'ProvisionedConcurrency': 123
                },
                'DesiredServerlessConfig': {
                    'MemorySizeInMB': 123,
                    'MaxConcurrency': 123,
                    'ProvisionedConcurrency': 123
                },
                'ManagedInstanceScaling': {
                    'Status': 'ENABLED'|'DISABLED',
                    'MinInstanceCount': 123,
                    'MaxInstanceCount': 123
                },
                'RoutingConfig': {
                    'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
                }
            },
        ]
    },
    'ExplainerConfig': {
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    'ShadowProductionVariants': [
        {
            'VariantName': 'string',
            'DeployedImages': [
                {
                    'SpecifiedImage': 'string',
                    'ResolvedImage': 'string',
                    'ResolutionTime': datetime(2015, 1, 1)
                },
            ],
            'CurrentWeight': ...,
            'DesiredWeight': ...,
            'CurrentInstanceCount': 123,
            'DesiredInstanceCount': 123,
            'VariantStatus': [
                {
                    'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
                    'StatusMessage': 'string',
                    'StartTime': datetime(2015, 1, 1)
                },
            ],
            'CurrentServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'DesiredServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ]
}

Response Structure

  • (dict) --

    • EndpointName (string) --

      Name of the endpoint.

    • EndpointArn (string) --

      The Amazon Resource Name (ARN) of the endpoint.

    • EndpointConfigName (string) --

      The name of the endpoint configuration associated with this endpoint.

    • ProductionVariants (list) --

      An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

      • (dict) --

        Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.

        • VariantName (string) --

          The name of the variant.

        • DeployedImages (list) --

          An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .

          • (dict) --

            Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .

            If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .

            • SpecifiedImage (string) --

              The image path you specified when you created the model.

            • ResolvedImage (string) --

              The specific digest path of the image hosted in this ProductionVariant .

            • ResolutionTime (datetime) --

              The date and time when the image path for the model resolved to the ResolvedImage

        • CurrentWeight (float) --

          The weight associated with the variant.

        • DesiredWeight (float) --

          The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

        • CurrentInstanceCount (integer) --

          The number of instances associated with the variant.

        • DesiredInstanceCount (integer) --

          The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

        • VariantStatus (list) --

          The endpoint variant status which describes the current deployment stage status or operational status.

          • (dict) --

            Describes the status of the production variant.

            • Status (string) --

              The endpoint variant status which describes the current deployment stage status or operational status.

              • Creating : Creating inference resources for the production variant.

              • Deleting : Terminating inference resources for the production variant.

              • Updating : Updating capacity for the production variant.

              • ActivatingTraffic : Turning on traffic for the production variant.

              • Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.

            • StatusMessage (string) --

              A message that describes the status of the production variant.

            • StartTime (datetime) --

              The start time of the current status change.

        • CurrentServerlessConfig (dict) --

          The serverless configuration for the endpoint.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • DesiredServerlessConfig (dict) --

          The serverless configuration requested for the endpoint update.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • DataCaptureConfig (dict) --

      The currently active data capture configuration used by your Endpoint.

      • EnableCapture (boolean) --

        Whether data capture is enabled or disabled.

      • CaptureStatus (string) --

        Whether data capture is currently functional.

      • CurrentSamplingPercentage (integer) --

        The percentage of requests being captured by your Endpoint.

      • DestinationS3Uri (string) --

        The Amazon S3 location being used to capture the data.

      • KmsKeyId (string) --

        The KMS key being used to encrypt the data in Amazon S3.

    • EndpointStatus (string) --

      The status of the endpoint.

      • OutOfService : Endpoint is not available to take incoming requests.

      • Creating : CreateEndpoint is executing.

      • Updating : UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

      • SystemUpdating : Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

      • RollingBack : Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

      • InService : Endpoint is available to process incoming requests.

      • Deleting : DeleteEndpoint is executing.

      • Failed : Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

      • UpdateRollbackFailed : Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status to InService , see Rolling Deployments .

    • FailureReason (string) --

      If the status of the endpoint is Failed , the reason why it failed.

    • CreationTime (datetime) --

      A timestamp that shows when the endpoint was created.

    • LastModifiedTime (datetime) --

      A timestamp that shows when the endpoint was last modified.

    • LastDeploymentConfig (dict) --

      The most recent deployment configuration for the endpoint.

      • BlueGreenUpdatePolicy (dict) --

        Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

        • TrafficRoutingConfiguration (dict) --

          Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.

          • Type (string) --

            Traffic routing strategy type.

            • ALL_AT_ONCE : Endpoint traffic shifts to the new fleet in a single step.

            • CANARY : Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.

            • LINEAR : Endpoint traffic shifts to the new fleet in n steps of a configurable size.

          • WaitIntervalInSeconds (integer) --

            The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.

          • CanarySize (dict) --

            Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.

            • Type (string) --

              Specifies the endpoint capacity type.

              • INSTANCE_COUNT : The endpoint activates based on the number of instances.

              • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

            • Value (integer) --

              Defines the capacity size, either as a number of instances or a capacity percentage.

          • LinearStepSize (dict) --

            Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.

            • Type (string) --

              Specifies the endpoint capacity type.

              • INSTANCE_COUNT : The endpoint activates based on the number of instances.

              • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

            • Value (integer) --

              Defines the capacity size, either as a number of instances or a capacity percentage.

        • TerminationWaitInSeconds (integer) --

          Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.

        • MaximumExecutionTimeoutInSeconds (integer) --

          Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds .

      • RollingUpdatePolicy (dict) --

        Specifies a rolling deployment strategy for updating a SageMaker endpoint.

        • MaximumBatchSize (dict) --

          Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.

          • Type (string) --

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.

            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

          • Value (integer) --

            Defines the capacity size, either as a number of instances or a capacity percentage.

        • WaitIntervalInSeconds (integer) --

          The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.

        • MaximumExecutionTimeoutInSeconds (integer) --

          The time limit for the total deployment. Exceeding this limit causes a timeout.

        • RollbackMaximumBatchSize (dict) --

          Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.

          • Type (string) --

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.

            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.

          • Value (integer) --

            Defines the capacity size, either as a number of instances or a capacity percentage.

      • AutoRollbackConfiguration (dict) --

        Automatic rollback configuration for handling endpoint deployment failures and recovery.

        • Alarms (list) --

          List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

          • (dict) --

            An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

            • AlarmName (string) --

              The name of a CloudWatch alarm in your account.

    • AsyncInferenceConfig (dict) --

      Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.

      • ClientConfig (dict) --

        Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

        • MaxConcurrentInvocationsPerInstance (integer) --

          The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

      • OutputConfig (dict) --

        Specifies the configuration for asynchronous inference invocation outputs.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

        • S3OutputPath (string) --

          The Amazon S3 location to upload inference responses to.

        • NotificationConfig (dict) --

          Specifies the configuration for notifications of inference results for asynchronous inference.

          • SuccessTopic (string) --

            Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

          • ErrorTopic (string) --

            Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

          • IncludeInferenceResponseIn (list) --

            The Amazon SNS topics where you want the inference response to be included.

            Note

            The inference response is included only if the response size is less than or equal to 128 KB.

            • (string) --

        • S3FailurePath (string) --

          The Amazon S3 location to upload failure inference responses to.

    • PendingDeploymentSummary (dict) --

      Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.

      • EndpointConfigName (string) --

        The name of the endpoint configuration used in the deployment.

      • ProductionVariants (list) --

        An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.

        • (dict) --

          The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

          • VariantName (string) --

            The name of the variant.

          • DeployedImages (list) --

            An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .

            • (dict) --

              Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .

              If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .

              • SpecifiedImage (string) --

                The image path you specified when you created the model.

              • ResolvedImage (string) --

                The specific digest path of the image hosted in this ProductionVariant .

              • ResolutionTime (datetime) --

                The date and time when the image path for the model resolved to the ResolvedImage

          • CurrentWeight (float) --

            The weight associated with the variant.

          • DesiredWeight (float) --

            The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • CurrentInstanceCount (integer) --

            The number of instances associated with the variant.

          • DesiredInstanceCount (integer) --

            The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • InstanceType (string) --

            The type of instances associated with the variant.

          • AcceleratorType (string) --

            The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

          • VariantStatus (list) --

            The endpoint variant status which describes the current deployment stage status or operational status.

            • (dict) --

              Describes the status of the production variant.

              • Status (string) --

                The endpoint variant status which describes the current deployment stage status or operational status.

                • Creating : Creating inference resources for the production variant.

                • Deleting : Terminating inference resources for the production variant.

                • Updating : Updating capacity for the production variant.

                • ActivatingTraffic : Turning on traffic for the production variant.

                • Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.

              • StatusMessage (string) --

                A message that describes the status of the production variant.

              • StartTime (datetime) --

                The start time of the current status change.

          • CurrentServerlessConfig (dict) --

            The serverless configuration for the endpoint.

            • MemorySizeInMB (integer) --

              The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

            • MaxConcurrency (integer) --

              The maximum number of concurrent invocations your serverless endpoint can process.

            • ProvisionedConcurrency (integer) --

              The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

              Note

              This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

          • DesiredServerlessConfig (dict) --

            The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.

            • MemorySizeInMB (integer) --

              The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

            • MaxConcurrency (integer) --

              The maximum number of concurrent invocations your serverless endpoint can process.

            • ProvisionedConcurrency (integer) --

              The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

              Note

              This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

          • ManagedInstanceScaling (dict) --

            Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

            • Status (string) --

              Indicates whether managed instance scaling is enabled.

            • MinInstanceCount (integer) --

              The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

            • MaxInstanceCount (integer) --

              The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

          • RoutingConfig (dict) --

            Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

            • RoutingStrategy (string) --

              Sets how the endpoint routes incoming traffic:

              • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

              • RANDOM : The endpoint routes each request to a randomly chosen instance.

      • StartTime (datetime) --

        The start time of the deployment.

      • ShadowProductionVariants (list) --

        An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.

        • (dict) --

          The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

          • VariantName (string) --

            The name of the variant.

          • DeployedImages (list) --

            An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .

            • (dict) --

              Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .

              If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .

              • SpecifiedImage (string) --

                The image path you specified when you created the model.

              • ResolvedImage (string) --

                The specific digest path of the image hosted in this ProductionVariant .

              • ResolutionTime (datetime) --

                The date and time when the image path for the model resolved to the ResolvedImage

          • CurrentWeight (float) --

            The weight associated with the variant.

          • DesiredWeight (float) --

            The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • CurrentInstanceCount (integer) --

            The number of instances associated with the variant.

          • DesiredInstanceCount (integer) --

            The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

          • InstanceType (string) --

            The type of instances associated with the variant.

          • AcceleratorType (string) --

            The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

          • VariantStatus (list) --

            The endpoint variant status which describes the current deployment stage status or operational status.

            • (dict) --

              Describes the status of the production variant.

              • Status (string) --

                The endpoint variant status which describes the current deployment stage status or operational status.

                • Creating : Creating inference resources for the production variant.

                • Deleting : Terminating inference resources for the production variant.

                • Updating : Updating capacity for the production variant.

                • ActivatingTraffic : Turning on traffic for the production variant.

                • Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.

              • StatusMessage (string) --

                A message that describes the status of the production variant.

              • StartTime (datetime) --

                The start time of the current status change.

          • CurrentServerlessConfig (dict) --

            The serverless configuration for the endpoint.

            • MemorySizeInMB (integer) --

              The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

            • MaxConcurrency (integer) --

              The maximum number of concurrent invocations your serverless endpoint can process.

            • ProvisionedConcurrency (integer) --

              The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

              Note

              This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

          • DesiredServerlessConfig (dict) --

            The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.

            • MemorySizeInMB (integer) --

              The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

            • MaxConcurrency (integer) --

              The maximum number of concurrent invocations your serverless endpoint can process.

            • ProvisionedConcurrency (integer) --

              The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

              Note

              This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

          • ManagedInstanceScaling (dict) --

            Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

            • Status (string) --

              Indicates whether managed instance scaling is enabled.

            • MinInstanceCount (integer) --

              The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

            • MaxInstanceCount (integer) --

              The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

          • RoutingConfig (dict) --

            Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

            • RoutingStrategy (string) --

              Sets how the endpoint routes incoming traffic:

              • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

              • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • ExplainerConfig (dict) --

      The configuration parameters for an explainer.

      • ClarifyExplainerConfig (dict) --

        A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

        • EnableExplanations (string) --

          A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

        • InferenceConfig (dict) --

          The inference configuration parameter for the model container.

          • FeaturesAttribute (string) --

            Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

          • ContentTemplate (string) --

            A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

          • MaxRecordCount (integer) --

            The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

          • MaxPayloadInMB (integer) --

            The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

          • ProbabilityIndex (integer) --

            A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

            Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

            Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

          • LabelIndex (integer) --

            A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

            Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

          • ProbabilityAttribute (string) --

            A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

            Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

          • LabelAttribute (string) --

            A JMESPath expression used to locate the list of label headers in the model container output.

            Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

          • LabelHeaders (list) --

            For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

            • (string) --

          • FeatureHeaders (list) --

            The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

          • FeatureTypes (list) --

            A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

        • ShapConfig (dict) --

          The configuration for SHAP analysis.

          • ShapBaselineConfig (dict) --

            The configuration for the SHAP baseline of the Kernal SHAP algorithm.

            • MimeType (string) --

              The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

            • ShapBaseline (string) --

              The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

            • ShapBaselineUri (string) --

              The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

          • NumberOfSamples (integer) --

            The number of samples to be used for analysis by the Kernal SHAP algorithm.

            Note

            The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

          • UseLogit (boolean) --

            A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

          • Seed (integer) --

            The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

          • TextConfig (dict) --

            A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

            • Language (string) --

              Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

              Note

              For a mix of multiple languages, use code 'xx' .

            • Granularity (string) --

              The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

    • ShadowProductionVariants (list) --

      An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .

      • (dict) --

        Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.

        • VariantName (string) --

          The name of the variant.

        • DeployedImages (list) --

          An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .

          • (dict) --

            Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .

            If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .

            • SpecifiedImage (string) --

              The image path you specified when you created the model.

            • ResolvedImage (string) --

              The specific digest path of the image hosted in this ProductionVariant .

            • ResolutionTime (datetime) --

              The date and time when the image path for the model resolved to the ResolvedImage

        • CurrentWeight (float) --

          The weight associated with the variant.

        • DesiredWeight (float) --

          The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

        • CurrentInstanceCount (integer) --

          The number of instances associated with the variant.

        • DesiredInstanceCount (integer) --

          The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

        • VariantStatus (list) --

          The endpoint variant status which describes the current deployment stage status or operational status.

          • (dict) --

            Describes the status of the production variant.

            • Status (string) --

              The endpoint variant status which describes the current deployment stage status or operational status.

              • Creating : Creating inference resources for the production variant.

              • Deleting : Terminating inference resources for the production variant.

              • Updating : Updating capacity for the production variant.

              • ActivatingTraffic : Turning on traffic for the production variant.

              • Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.

            • StatusMessage (string) --

              A message that describes the status of the production variant.

            • StartTime (datetime) --

              The start time of the current status change.

        • CurrentServerlessConfig (dict) --

          The serverless configuration for the endpoint.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • DesiredServerlessConfig (dict) --

          The serverless configuration requested for the endpoint update.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

DescribeEndpointConfig (updated) Link ¶
Changes (response)
{'DataCaptureConfig': {'CaptureOptions': {'CaptureMode': {'InputAndOutput'}}},
 'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                         'ml.c7i.16xlarge',
                                         'ml.c7i.24xlarge',
                                         'ml.c7i.2xlarge',
                                         'ml.c7i.48xlarge',
                                         'ml.c7i.4xlarge',
                                         'ml.c7i.8xlarge',
                                         'ml.c7i.large',
                                         'ml.c7i.xlarge',
                                         'ml.dl1.24xlarge',
                                         'ml.m7i.12xlarge',
                                         'ml.m7i.16xlarge',
                                         'ml.m7i.24xlarge',
                                         'ml.m7i.2xlarge',
                                         'ml.m7i.48xlarge',
                                         'ml.m7i.4xlarge',
                                         'ml.m7i.8xlarge',
                                         'ml.m7i.large',
                                         'ml.m7i.xlarge',
                                         'ml.r7i.12xlarge',
                                         'ml.r7i.16xlarge',
                                         'ml.r7i.24xlarge',
                                         'ml.r7i.2xlarge',
                                         'ml.r7i.48xlarge',
                                         'ml.r7i.4xlarge',
                                         'ml.r7i.8xlarge',
                                         'ml.r7i.large',
                                         'ml.r7i.xlarge',
                                         'ml.trn1n.32xlarge'}},
 'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
                                               'ml.c7i.16xlarge',
                                               'ml.c7i.24xlarge',
                                               'ml.c7i.2xlarge',
                                               'ml.c7i.48xlarge',
                                               'ml.c7i.4xlarge',
                                               'ml.c7i.8xlarge',
                                               'ml.c7i.large',
                                               'ml.c7i.xlarge',
                                               'ml.dl1.24xlarge',
                                               'ml.m7i.12xlarge',
                                               'ml.m7i.16xlarge',
                                               'ml.m7i.24xlarge',
                                               'ml.m7i.2xlarge',
                                               'ml.m7i.48xlarge',
                                               'ml.m7i.4xlarge',
                                               'ml.m7i.8xlarge',
                                               'ml.m7i.large',
                                               'ml.m7i.xlarge',
                                               'ml.r7i.12xlarge',
                                               'ml.r7i.16xlarge',
                                               'ml.r7i.24xlarge',
                                               'ml.r7i.2xlarge',
                                               'ml.r7i.48xlarge',
                                               'ml.r7i.4xlarge',
                                               'ml.r7i.8xlarge',
                                               'ml.r7i.large',
                                               'ml.r7i.xlarge',
                                               'ml.trn1n.32xlarge'}}}

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

See also: AWS API Documentation

Request Syntax

client.describe_endpoint_config(
    EndpointConfigName='string'
)
type EndpointConfigName

string

param EndpointConfigName

[REQUIRED]

The name of the endpoint configuration.

rtype

dict

returns

Response Syntax

{
    'EndpointConfigName': 'string',
    'EndpointConfigArn': 'string',
    'ProductionVariants': [
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    'DataCaptureConfig': {
        'EnableCapture': True|False,
        'InitialSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string',
        'CaptureOptions': [
            {
                'CaptureMode': 'Input'|'Output'|'InputAndOutput'
            },
        ],
        'CaptureContentTypeHeader': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    'KmsKeyId': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'AsyncInferenceConfig': {
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string',
                'IncludeInferenceResponseIn': [
                    'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
                ]
            },
            'S3FailurePath': 'string'
        }
    },
    'ExplainerConfig': {
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    'ShadowProductionVariants': [
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123,
                'ProvisionedConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123,
            'EnableSSMAccess': True|False,
            'ManagedInstanceScaling': {
                'Status': 'ENABLED'|'DISABLED',
                'MinInstanceCount': 123,
                'MaxInstanceCount': 123
            },
            'RoutingConfig': {
                'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
            }
        },
    ],
    'ExecutionRoleArn': 'string',
    'VpcConfig': {
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    'EnableNetworkIsolation': True|False
}

Response Structure

  • (dict) --

    • EndpointConfigName (string) --

      Name of the SageMaker endpoint configuration.

    • EndpointConfigArn (string) --

      The Amazon Resource Name (ARN) of the endpoint configuration.

    • ProductionVariants (list) --

      An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

      • (dict) --

        Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

        • VariantName (string) --

          The name of the production variant.

        • ModelName (string) --

          The name of the model that you want to host. This is the name that you specified when creating the model.

        • InitialInstanceCount (integer) --

          Number of instances to launch initially.

        • InstanceType (string) --

          The ML compute instance type.

        • InitialVariantWeight (float) --

          Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

        • AcceleratorType (string) --

          The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

        • CoreDumpConfig (dict) --

          Specifies configuration for a core dump from the model container when the process crashes.

          • DestinationS3Uri (string) --

            The Amazon S3 bucket to send the core dump to.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

            • // KMS Key Alias "alias/ExampleAlias"

            • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

            If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

            The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • ServerlessConfig (dict) --

          The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • VolumeSizeInGB (integer) --

          The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

        • ModelDataDownloadTimeoutInSeconds (integer) --

          The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

        • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

          The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

        • EnableSSMAccess (boolean) --

          You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • DataCaptureConfig (dict) --

      Configuration to control how SageMaker captures inference data.

      • EnableCapture (boolean) --

        Whether data capture should be enabled or disabled (defaults to enabled).

      • InitialSamplingPercentage (integer) --

        The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

      • DestinationS3Uri (string) --

        The Amazon S3 location used to capture the data.

      • KmsKeyId (string) --

        The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.

        The KmsKeyId can be any of the following formats:

        • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

        • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

        • Alias name: alias/ExampleAlias

        • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

      • CaptureOptions (list) --

        Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

        • (dict) --

          Specifies data Model Monitor will capture.

          • CaptureMode (string) --

            Specify the boundary of data to capture.

      • CaptureContentTypeHeader (dict) --

        Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

        • CsvContentTypes (list) --

          The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.

          • (string) --

        • JsonContentTypes (list) --

          The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

          • (string) --

    • KmsKeyId (string) --

      Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

    • CreationTime (datetime) --

      A timestamp that shows when the endpoint configuration was created.

    • AsyncInferenceConfig (dict) --

      Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.

      • ClientConfig (dict) --

        Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

        • MaxConcurrentInvocationsPerInstance (integer) --

          The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

      • OutputConfig (dict) --

        Specifies the configuration for asynchronous inference invocation outputs.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

        • S3OutputPath (string) --

          The Amazon S3 location to upload inference responses to.

        • NotificationConfig (dict) --

          Specifies the configuration for notifications of inference results for asynchronous inference.

          • SuccessTopic (string) --

            Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

          • ErrorTopic (string) --

            Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

          • IncludeInferenceResponseIn (list) --

            The Amazon SNS topics where you want the inference response to be included.

            Note

            The inference response is included only if the response size is less than or equal to 128 KB.

            • (string) --

        • S3FailurePath (string) --

          The Amazon S3 location to upload failure inference responses to.

    • ExplainerConfig (dict) --

      The configuration parameters for an explainer.

      • ClarifyExplainerConfig (dict) --

        A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

        • EnableExplanations (string) --

          A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

        • InferenceConfig (dict) --

          The inference configuration parameter for the model container.

          • FeaturesAttribute (string) --

            Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

          • ContentTemplate (string) --

            A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

          • MaxRecordCount (integer) --

            The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

          • MaxPayloadInMB (integer) --

            The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

          • ProbabilityIndex (integer) --

            A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

            Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

            Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

          • LabelIndex (integer) --

            A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

            Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .

          • ProbabilityAttribute (string) --

            A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

            Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .

          • LabelAttribute (string) --

            A JMESPath expression used to locate the list of label headers in the model container output.

            Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

          • LabelHeaders (list) --

            For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

            • (string) --

          • FeatureHeaders (list) --

            The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

          • FeatureTypes (list) --

            A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

            • (string) --

        • ShapConfig (dict) --

          The configuration for SHAP analysis.

          • ShapBaselineConfig (dict) --

            The configuration for the SHAP baseline of the Kernal SHAP algorithm.

            • MimeType (string) --

              The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

            • ShapBaseline (string) --

              The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

            • ShapBaselineUri (string) --

              The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .

          • NumberOfSamples (integer) --

            The number of samples to be used for analysis by the Kernal SHAP algorithm.

            Note

            The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .

          • UseLogit (boolean) --

            A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

          • Seed (integer) --

            The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

          • TextConfig (dict) --

            A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

            • Language (string) --

              Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

              Note

              For a mix of multiple languages, use code 'xx' .

            • Granularity (string) --

              The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

    • ShadowProductionVariants (list) --

      An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .

      • (dict) --

        Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .

        • VariantName (string) --

          The name of the production variant.

        • ModelName (string) --

          The name of the model that you want to host. This is the name that you specified when creating the model.

        • InitialInstanceCount (integer) --

          Number of instances to launch initially.

        • InstanceType (string) --

          The ML compute instance type.

        • InitialVariantWeight (float) --

          Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

        • AcceleratorType (string) --

          The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .

        • CoreDumpConfig (dict) --

          Specifies configuration for a core dump from the model container when the process crashes.

          • DestinationS3Uri (string) --

            The Amazon S3 bucket to send the core dump to.

          • KmsKeyId (string) --

            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

            • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

            • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

            • // KMS Key Alias "alias/ExampleAlias"

            • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

            If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

            The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • ServerlessConfig (dict) --

          The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

          • MemorySizeInMB (integer) --

            The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

          • MaxConcurrency (integer) --

            The maximum number of concurrent invocations your serverless endpoint can process.

          • ProvisionedConcurrency (integer) --

            The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .

            Note

            This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .

        • VolumeSizeInGB (integer) --

          The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

        • ModelDataDownloadTimeoutInSeconds (integer) --

          The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

        • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

          The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .

        • EnableSSMAccess (boolean) --

          You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .

        • ManagedInstanceScaling (dict) --

          Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

          • Status (string) --

            Indicates whether managed instance scaling is enabled.

          • MinInstanceCount (integer) --

            The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

          • MaxInstanceCount (integer) --

            The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

        • RoutingConfig (dict) --

          Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

          • RoutingStrategy (string) --

            Sets how the endpoint routes incoming traffic:

            • LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.

            • RANDOM : The endpoint routes each request to a randomly chosen instance.

    • ExecutionRoleArn (string) --

      The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.

    • VpcConfig (dict) --

      Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .

      • SecurityGroupIds (list) --

        The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

        • (string) --

      • Subnets (list) --

        The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

        • (string) --

    • EnableNetworkIsolation (boolean) --

      Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

DescribeFeatureGroup (updated) Link ¶
Changes (response)
{'OfflineStoreConfig': {'TableFormat': {'Default'}}}

Use this operation to describe a FeatureGroup . The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup , and more.

See also: AWS API Documentation

Request Syntax

client.describe_feature_group(
    FeatureGroupName='string',
    NextToken='string'
)
type FeatureGroupName

string

param FeatureGroupName

[REQUIRED]

The name or Amazon Resource Name (ARN) of the FeatureGroup you want described.

type NextToken

string

param NextToken

A token to resume pagination of the list of Features (FeatureDefinitions ). 2,500 Features are returned by default.

rtype

dict

returns

Response Syntax

{
    'FeatureGroupArn': 'string',
    'FeatureGroupName': 'string',
    'RecordIdentifierFeatureName': 'string',
    'EventTimeFeatureName': 'string',
    'FeatureDefinitions': [
        {
            'FeatureName': 'string',
            'FeatureType': 'Integral'|'Fractional'|'String',
            'CollectionType': 'List'|'Set'|'Vector',
            'CollectionConfig': {
                'VectorConfig': {
                    'Dimension': 123
                }
            }
        },
    ],
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'OnlineStoreConfig': {
        'SecurityConfig': {
            'KmsKeyId': 'string'
        },
        'EnableOnlineStore': True|False,
        'TtlDuration': {
            'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
            'Value': 123
        },
        'StorageType': 'Standard'|'InMemory'
    },
    'OfflineStoreConfig': {
        'S3StorageConfig': {
            'S3Uri': 'string',
            'KmsKeyId': 'string',
            'ResolvedOutputS3Uri': 'string'
        },
        'DisableGlueTableCreation': True|False,
        'DataCatalogConfig': {
            'TableName': 'string',
            'Catalog': 'string',
            'Database': 'string'
        },
        'TableFormat': 'Default'|'Glue'|'Iceberg'
    },
    'RoleArn': 'string',
    'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
    'OfflineStoreStatus': {
        'Status': 'Active'|'Blocked'|'Disabled',
        'BlockedReason': 'string'
    },
    'LastUpdateStatus': {
        'Status': 'Successful'|'Failed'|'InProgress',
        'FailureReason': 'string'
    },
    'FailureReason': 'string',
    'Description': 'string',
    'NextToken': 'string',
    'OnlineStoreTotalSizeBytes': 123
}

Response Structure

  • (dict) --

    • FeatureGroupArn (string) --

      The Amazon Resource Name (ARN) of the FeatureGroup .

    • FeatureGroupName (string) --

      he name of the FeatureGroup .

    • RecordIdentifierFeatureName (string) --

      The name of the Feature used for RecordIdentifier , whose value uniquely identifies a record stored in the feature store.

    • EventTimeFeatureName (string) --

      The name of the feature that stores the EventTime of a Record in a FeatureGroup .

      An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup have a corresponding EventTime .

    • FeatureDefinitions (list) --

      A list of the Features in the FeatureGroup . Each feature is defined by a FeatureName and FeatureType .

      • (dict) --

        A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .

        • FeatureName (string) --

          The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .

        • FeatureType (string) --

          The value type of a feature.